Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "Lee, C." wg kryterium: Autor

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-2 z 2
    Tytuł:
    Enhancement of Drilling Safety and Quality Using Online Sensors and Artificial Neural Networks
    Autorzy:
    Liu, T.- I.
    Kumagai, A.
    Lee, C.
    Powiązania:
    https://bibliotekanauki.pl/articles/90529.pdf
    Data publikacji:
    2003
    Wydawca:
    Centralny Instytut Ochrony Pracy
    Tematy:
    drilling safety
    online sensors
    artificial neural networks
    bezpieczeństwo pracy
    ocena ryzyka zawodowego
    wiertnictwo
    sieci neuronowe
    Opis:
    Cutting force sensors and neural networks have been used for the occupational safety of the drilling process. The drill conditions have been online classified into 3 categories: safe, caution, and danger. This approach can change the drill just before its failure. The inputs to neural networks include drill size, feed rate, spindle speed, and features that were extracted from drilling force measure-ments. The outputs indicate the safety states. This detection system can reach a success rate of over 95%. Furthermore, the one misclassification during online tests was a one-step ahead pre-alarm that is acceptable from the safety and quality viewpoint. The developed online detection system is very robust and can be used in very complex manufacturing environments.
    Źródło:
    International Journal of Occupational Safety and Ergonomics; 2003, 9, 1; 37-56
    1080-3548
    Pojawia się w:
    International Journal of Occupational Safety and Ergonomics
    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 1-2 z 2

      Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies