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Wyszukujesz frazę "Osowski, S." wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
Local dynamic integration of ensemble in prediction of time series
Autorzy:
Osowski, S.
Siwek, K.
Powiązania:
https://bibliotekanauki.pl/articles/201557.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
neural networks
ensemble of predictors
dynamic integration
time series prediction
sieci neuronowe
zespół predyktorów
dynamiczna integracja
Opis:
The paper presents local dynamic approach to integration of an ensemble of predictors. The classical fusing of many predictor results takes into account all units and takes the weighted average of the results of all units forming the ensemble. This paper proposes different approach. The prediction of time series for the next day is done here by only one member of an ensemble, which was the best in the learning stage for the input vector, closest to the input data actually applied. Thanks to such arrangement we avoid the situation in which the worst unit reduces the accuracy of the whole ensemble. This way we obtain an increased level of statistical forecasting accuracy, since each task is performed by the best suited predictor. Moreover, such arrangement of integration allows for using units of very different quality without decreasing the quality of final prediction. The numerical experiments performed for forecasting the next input, the average PM10 pollution and forecasting the 24-element vector of hourly load of the power system have confirmed the superiority of the presented approach. All quality measures of forecast have been significantly improved.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2019, 67, 3; 517-525
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Developing automatic recognition system of drill wear in standard laminated chipboard drilling process
Autorzy:
Kurek, J.
Kruk, M.
Osowski, S.
Hoser, P.
Wieczorek, G.
Jegorowa, A.
Górski, J.
Wilkowski, J.
Śmietańska, K.
Kossakowska, J.
Powiązania:
https://bibliotekanauki.pl/articles/200766.pdf
Data publikacji:
2016
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
diagnostic expert systems
neural networks
wavelet packets
wear monitoring
diagnostyczny system ekspercki
sieci neuronowe
pakiety falkowe
monitorowanie zużycia
Opis:
The paper presents an automatic approach to recognition of the drill condition in a standard laminated chipboard drilling process. The state of the drill is classified into two classes: “useful” (sharp enough) and “useless” (worn out). The case “useless” indicates symptoms of excessive drill wear, unsatisfactory from the point of view of furniture processing quality. On the other hand the “useful” state identifies tools which are still able to drill holes acceptable due to the required processing quality. The main problem in this task is to choose an appropriate set of diagnostic features (variables), based on which the recognition of drill state (“useful” versus “useless”) can be made. The features have been generated based on 5 registered signals: feed force, cutting torque, noise, vibration and acoustic emission. Different statistical parameters describing these signals and also their Fourier and wavelet representations have been used for defining the features. Sequential feature selection is applied to detect the most class discriminative set of features. The final step of recognition is done by using three types of classifiers, including support vector machine, ensemble of decision trees and random forest. Six standard drills of 12 mm diameter with tungsten carbide tips were used in experiments. The results have confirmed good quality of the proposed diagnostic system.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2016, 64, 3; 633-640
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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