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    Wyświetlanie 1-5 z 5
    Tytuł:
    Diagnostics of induction motor based on analysis of acoustic signals with the application of eigenvector method and K-Nearest Neighbor classifier
    Diagnostyka silnika indukcyjnego oparta na analizie sygnałów akustycznych z zastosowaniem metody wektora własnego i klasyfikatora K-Najbliższego Sąsiada
    Autorzy:
    Głowacz, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/355094.pdf
    Data publikacji:
    2012
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    diagnostyka silnika indukcyjnego
    silniki indukcyjne
    sygnały akustyczne
    diagnostics
    recognition
    acoustic signal
    induction motor
    Opis:
    In this paper numerical experiments are proposed to investigate differences between the acoustic signals of induction motors. Four conditions of induction motor were considered. Investigations were carried out with application of eigenvector method and K-Nearest Neighbor classifier with Minkowski distance. Pattern creation process was conducted for 20 samples of sound. Identification process used 96 samples of sound. The obtained results confirm the correctness of the solutions methodology.
    W tym artykule eksperymenty numeryczne są proponowane w celu zbadania różnic między sygnałami akustycznymi silników indukcyjnych. Rozważano cztery stany silnika indukcyjnego. Badania zostały przeprowadzone z zastosowaniem metody wektora własnego i klasyfikatora K-Najbliższego Sąsiada z metryka Minkowskiego. Proces tworzenia wzorców do rozpoznawania został przeprowadzony dla 20 próbek dźwięku. Proces identyfikacji wykorzystywał 96 próbek dźwięku. Uzyskane efekty potwierdzają poprawność rozwiązań metodycznych.
    Źródło:
    Archives of Metallurgy and Materials; 2012, 57, 2; 403-407
    1733-3490
    Pojawia się w:
    Archives of Metallurgy and Materials
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A differential evolution approach to dimensionality reduction for classification needs
    Autorzy:
    Martinović, G.
    Bajer, D.
    Zorić, B.
    Powiązania:
    https://bibliotekanauki.pl/articles/331498.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    classification
    differential evolution
    feature subset selection
    k-nearest neighbour algorithm
    wrapper method
    ewolucja różnicowa
    selekcja cech
    algorytm najbliższego sąsiada
    Opis:
    The feature selection problem often occurs in pattern recognition and, more specifically, classification. Although these patterns could contain a large number of features, some of them could prove to be irrelevant, redundant or even detrimental to classification accuracy. Thus, it is important to remove these kinds of features, which in turn leads to problem dimensionality reduction and could eventually improve the classification accuracy. In this paper an approach to dimensionality reduction based on differential evolution which represents a wrapper and explores the solution space is presented. The solutions, subsets of the whole feature set, are evaluated using the k-nearest neighbour algorithm. High quality solutions found during execution of the differential evolution fill the archive. A final solution is obtained by conducting k-fold cross-validation on the archive solutions and selecting the best one. Experimental analysis is conducted on several standard test sets. The classification accuracy of the k-nearest neighbour algorithm using the full feature set and the accuracy of the same algorithm using only the subset provided by the proposed approach and some other optimization algorithms which were used as wrappers are compared. The analysis shows that the proposed approach successfully determines good feature subsets which may increase the classification accuracy.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2014, 24, 1; 111-122
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An adaptive k nearest neighbour method for imputation of missing traffic data based on two similarity metrics
    Autorzy:
    Wang, Yang
    Xiao, Yu
    Lai, Jianhui
    Chen, Yanyan
    Powiązania:
    https://bibliotekanauki.pl/articles/949848.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    missing traffic data
    similarity metrics
    K-nearest neighbour method
    stochastic characteristics
    metoda porównywania danych
    metryki podobieństwa
    metoda najbliższego sąsiada
    cechy stochastyczne
    Opis:
    Traffic flow is one of the fundamental parameters for traffic analysis and planning. With the rapid development of intelligent transportation systems, a large number of various detectors have been deployed in urban roads and, consequently, huge amount of data relating to the traffic flow are accumulatively available now. However, the traffic flow data detected through various detectors are often degraded due to the presence of a number of missing data, which can even lead to erroneous analysis and decision if no appropriate process is carried out. To remedy this issue, great research efforts have been made and subsequently various imputation techniques have been successively proposed in recent years, among which the k nearest neighbour algorithm (kNN) has received a great popularity as it is easy to implement and impute the missing data effectively. In the work presented in this paper, we firstly analyse the stochastic effect of traffic flow, to which the suffering of the kNN algorithm can be attributed. This motivates us to make an improvement, while eliminating the requirement to predefine parameters. Such a parameter-free algorithm has been realized by introducing a new similarity metric which is combined with the conventional metric so as to avoid the parameter setting, which is often determined with the requirement of adequate domain knowledge. Unlike the conventional version of the kNN algorithm, the proposed algorithm employs the multivariate linear regression model to estimate the weights for the final output, based on a set of data, which is smoothed by a Wavelet technique. A series of experiments have been performed, based on a set of traffic flow data reported from serval different countries, to examine the adaptive determination of parameters and the smoothing effect. Additional experiments have been conducted to evaluate the competent performance for the proposed algorithm by comparing to a number of widely-used imputing algorithms.
    Źródło:
    Archives of Transport; 2020, 54, 2; 59-73
    0866-9546
    2300-8830
    Pojawia się w:
    Archives of Transport
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A practical application of kernel-based fuzzy discriminant analysis
    Autorzy:
    Gao, J. Q.
    Fan, L. Y.
    Li, L.
    Xu, L. Z.
    Powiązania:
    https://bibliotekanauki.pl/articles/908344.pdf
    Data publikacji:
    2013
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    analiza dyskryminacyjna
    algorytm najbliższego sąsiada
    SVD
    kernel fuzzy discriminant analysis
    fuzzy k-nearest neighbor
    QR decomposition
    singular value decomposition (SVD)
    fuzzy membership matrix
    t-test
    Opis:
    A novel method for feature extraction and recognition called Kernel Fuzzy Discriminant Analysis (KFDA) is proposed in this paper to deal with recognition problems, e.g., for images. The KFDA method is obtained by combining the advantages of fuzzy methods and a kernel trick. Based on the orthogonal-triangular decomposition of a matrix and Singular Value Decomposition (SVD), two different variants, KFDA/QR and KFDA/SVD, of KFDA are obtained. In the proposed method, the membership degree is incorporated into the definition of between-class and within-class scatter matrices to get fuzzy between-class and within-class scatter matrices. The membership degree is obtained by combining the measures of features of samples data. In addition, the effects of employing different measures is investigated from a pure mathematical point of view, and the t-test statistical method is used for comparing the robustness of the learning algorithm. Experimental results on ORL and FERET face databases show that KFDA/QR and KFDA/SVD are more effective and feasible than Fuzzy Discriminant Analysis (FDA) and Kernel Discriminant Analysis (KDA) in terms of the mean correct recognition rate.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2013, 23, 4; 887-903
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Non-parametric machine learning methods for evaluating the effects of traffic accident duration on freeways
    Autorzy:
    Lee, Y.
    Wei, C.-H.
    Chao, K.-C.
    Powiązania:
    https://bibliotekanauki.pl/articles/223569.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    accident on freeway
    accident duration
    effect evaluating
    correlation
    artificial neural networks
    k-nearest neighbour method
    wypadek na autostradzie
    czas trwania wypadku
    ocena skutków
    korelacja
    sztuczne sieci neuronowe
    metoda najbliższego sąsiada
    Opis:
    Traffic accidents usually cause congestion and increase travel-times. The cost of extra travel time and fuel consumption due to congestion is huge. Traffic operators and drivers expect an accurately forecasted accident duration to reduce uncertainty and to enable the implementation of appropriate strategies. This study demonstrates two non-parametric machine learning methods, namely the k-nearest neighbour method and artificial neural network method, to construct accident duration prediction models. The factors influencing the occurrence of accidents are numerous and complex. To capture this phenomenon and improve the performance of accident duration prediction, the models incorporated various data including accident characteristics, traffic data, illumination, weather conditions, and road geometry characteristics. All raw data are collected from two public agencies and were integrated and cross-checked. Before model development, a correlation analysis was performed to reduce the scale of interrelated features or variables. Based on the performance comparison results, an artificial neural network model can provide good and reasonable prediction for accident duration with mean absolute percentage error values less than 30%, which are better than the prediction results of a k-nearest neighbour model. Based on comparison results for circumstances, the Model which incorporated significant variables and employed the ANN method can provide a more accurate prediction of accident duration when the circumstances involved the day time or drunk driving than those that involved night time and did not involve drunk driving. Empirical evaluation results reveal that significant variables possess a major influence on accident duration prediction.
    Źródło:
    Archives of Transport; 2017, 43, 3; 91-104
    0866-9546
    2300-8830
    Pojawia się w:
    Archives of Transport
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-5 z 5

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