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    Wyświetlanie 1-3 z 3
    Tytuł:
    Comparison of speaker dependent and speaker independent emotion recognition
    Autorzy:
    Rybka, J.
    Janicki, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/330055.pdf
    Data publikacji:
    2013
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    speech processing
    emotion recognition
    EMO-DB
    support vector machines
    artificial neural network
    przetwarzanie mowy
    rozpoznawanie emocji
    maszyna wektorów wspierających
    sztuczna sieć neuronowa
    Opis:
    This paper describes a study of emotion recognition based on speech analysis. The introduction to the theory contains a review of emotion inventories used in various studies of emotion recognition as well as the speech corpora applied, methods of speech parametrization, and the most commonly employed classification algorithms. In the current study the EMO-DB speech corpus and three selected classifiers, the k-Nearest Neighbor (k-NN), the Artificial Neural Network (ANN) and Support Vector Machines (SVMs), were used in experiments. SVMs turned out to provide the best classification accuracy of 75.44% in the speaker dependent mode, that is, when speech samples from the same speaker were included in the training corpus. Various speaker dependent and speaker independent configurations were analyzed and compared. Emotion recognition in speaker dependent conditions usually yielded higher accuracy results than a similar but speaker independent configuration. The improvement was especially well observed if the base recognition ratio of a given speaker was low. Happiness and anger, as well as boredom and neutrality, proved to be the pairs of emotions most often confused.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2013, 23, 4; 797-808
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Estimating the compressive strength of concrete, using vacuum dewatering technique
    Autorzy:
    Subhash, D.
    Gupta, S. M.
    Setia, S.
    Pavlykivskyi, V.
    Powiązania:
    https://bibliotekanauki.pl/articles/378711.pdf
    Data publikacji:
    2019
    Wydawca:
    Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
    Tematy:
    vacuum dewatering
    concrete compressive strength
    artificial neural network
    Support Vector Machine
    odwadnianie próżniowe
    wytrzymałość betonu na ściskanie
    sztuczna sieć neuronowa
    maszyna wektorów wspierających
    Opis:
    Purpose: Investigate the potential of vacuum dewatering process of on three different grades of concrete namely M20, M30 and M40 to evaluate its compressive strength. Design/methodology/approach: For this study a data set of 90 experimental observations obtained from laboratory testing with and without application of vacuum dewatering after designing and casting the concrete of said three grades. The standard cubes of size 150 mm × 150 mm × 150 mm were obtained by core cutting and tested for compression after 3, 7, 14, 21 and 28 days of proper curing. Accuracy of prediction of compressive strength of concrete by application of M5P, ANN and SVM as artificial intelligence techniques and their feasibility are assessed to estimate the compressive strength of the concrete enacted with vacuum dewatering technique. A total data set was segregated in two groups. A group of 63 observations was used for model development and smaller group of 27 observations was used for testing the models. Findings: Overall performance of ANN based developed model is better than M5P and SVM based models for predicting the compressive strength of concrete for this data set. Research limitations/implications: Investigated three different grades of concrete namely M20, M30 and M40 to evaluate its compressive strength. The experimental research involved only testing of cubes only. Practical implications: Using ANN based developed model makes it possible to quickly and accurately predict the compressive strength of concrete. Originality/value: The results of comparing three models for predicting the compressive strength of concrete and the optimal values of ANN based developed models are presented. Earlier no one has applied M5P, ANN and SVM modelling to predict the compressive strength of vacuum dewatered concrete.
    Źródło:
    Archives of Materials Science and Engineering; 2019, 99, 1/2; 30-41
    1897-2764
    Pojawia się w:
    Archives of Materials Science and Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Adaptive control scheme based on the least squares support vector machine network
    Autorzy:
    Mahmoud, T. K.
    Powiązania:
    https://bibliotekanauki.pl/articles/930155.pdf
    Data publikacji:
    2011
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    modelowanie systemu
    system nieliniowy
    system sterowania
    sieć neuronowa
    maszyna wektorów wspierających
    support vector machine (SVM)
    neural network
    nonlinear system modeling
    nonlinear system control
    pH control
    Opis:
    Recently, a new type of neural networks called Least Squares Support Vector Machines (LS-SVMs) has been receiving increasing attention in nonlinear system identification and control due to its generalization performance. This paper develops a stable adaptive control scheme using the LS-SVM network. The developed control scheme includes two parts: the identification part that uses a modified structure of LS-SVM neural networks called the multi-resolution wavelet least squares support vector machine network (MRWLS-SVM) as a predictor model, and the controller part that is developed to track a reference trajectory. By means of the Lyapunov stability criterion, stability analysis for the tracking errors is performed. Finally, simulation studies are performed to demonstrate the capability of the developed approach in controlling a pH process.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2011, 21, 4; 685-696
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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