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 "transport technology" wg kryterium: Temat

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-2 z 2
    Tytuł:
    Conceptual Model Creation for Automated Self-training System of Functional Control and Detection of Railway Transport
    Autorzy:
    Oralbekova, Ayaulym
    Amanova, Marzhana
    Rustambekova, Kamila
    Kaskatayev, Zhanat
    Kisselyova, Olga
    Nurgaliyeva, Roza
    Powiązania:
    https://bibliotekanauki.pl/articles/2055269.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    railway transport
    system of components and assemblies
    decision support systems
    intelligent technology
    automatic detection systems
    object used for training
    Opis:
    In order to improve the operational reliability and service life of the main systems, components and assemblies (SCA) of railway transport (RT), it is necessary to timely detect (diagnose) their defects, including the use of the methods of intellectual analysis and data processing. One of the promising approaches to the synthesis of the SCA functional control system is the use of intelligent technology (INTECH) methods. This technology is based on maximizing the information capacity of an automated decision support system for detecting faults during its training.
    Źródło:
    International Journal of Electronics and Telecommunications; 2021, 67, 4; 603--608
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Automated Self-trained System of Functional Control and State Detection of Railway Transport Nodes
    Autorzy:
    Akhmetov, Bakhytzhan
    Lakhno, Valeriy
    Oralbekova, Ayaulym
    Kaskatayev, Zhanat
    Mussayeva, Gulmira
    Powiązania:
    https://bibliotekanauki.pl/articles/227158.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    information intellectual technology of failure detection
    functional control
    learning matrix
    learning algorithm
    operator effectiveness criterion
    nodes and aggregates
    railway transport
    Opis:
    Automation of data processing of contactless diagnostics (detection) of the technical condition of the majority of nodes and aggregates of railway transport (RWT) minimizes the damage from failures of these systems in operating modes. This becomes possible due to the rapid detection of serious defects at the stage of their origin. Basically, in practice, the control of the technical condition of the nodes and aggregates of the RWT is carried out during scheduled repairs. It is not always possible to identify incipient defects. Consequently, it is not always possible to warn personnel (machinists, repairmen, etc.) of significant damage to the RWT systems until their complete failure. The difficulties of obtaining diagnostic information is that there is interdependence between the main nodes of the RWT. This means that if physical damage occurs at any of the RWT nodes, in other nodes there can also occur malfunctions. As the main way to improve the efficiency of state detection of the nodes and aggregates of RWT, we see the direction of giving the adaptability property for an automated data processing system from various contactless diagnostic information removal systems. The global purpose can be achieved, in particular, through the use of machine learning methods and failure recognition (recognition objects). In order to improve the operational reliability and service life of the main nodes and aggregates of RWT, there are proposed an appropriate model and algorithm of machine learning of the operator control system of nodes and aggregates. It is proposed to use the Shannon normalized entropy measure and the Kullback-Leibler distance information criterion as a criterion of the learning effectiveness of the automated detection system and operator node state control of RWT. The article describes the application of the proposed method on the example of an automatic detection system (ADS) of the state of a traction motor of an electric locomotive. There are given the test data of the model and algorithm in the MATLAB environment.
    Źródło:
    International Journal of Electronics and Telecommunications; 2019, 65, 3; 491-496
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    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