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 "intelligent autonomous ship navigation" wg kryterium: Temat

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-3 z 3
    Tytuł:
    Intelligent autonomous ship navigation using multi-sensor modalities
    Autorzy:
    Wright, R.G.
    Powiązania:
    https://bibliotekanauki.pl/articles/117337.pdf
    Data publikacji:
    2019
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    maritime autonomous surface ships (MASS)
    artificial intelligence (AI)
    intelligent autonomous ship navigation
    autonomous ship
    autonomous ships navigation
    multiSensor modalities
    unmanned ship
    maritime domain awareness (MDA)
    Opis:
    This paper explores the use of machine learning and deep learning artificial intelligence (AI) techniques as a means to integrate multiple sensor modalities into a cohesive approach to navigation for autonomous ships. Considered is the case of a fully autonomous ship capable of making decisions and determining actions by itself without active supervision on the part of onboard crew or remote human operators. These techniques, when combined with advanced sensor capabilities, have been touted as a means to overcome existing technical and human limitations as unmanned and autonomous ships become operational presently and in upcoming years. Promises of the extraordinary capabilities of these technologies that may even exceed those of crewmembers for decision making under comparable conditions must be tempered with realistic expectations as to their ultimate technical potential, their use in the maritime domain, vulnerabilities that may preclude their safe operation; and methods for development, integration and test. The results of research performed by the author in specific applications of machine learning and AI to shipping are presented citing key factors that must be achieved for certification of these technologies as being suitable for their intended purpose. Recommendations are made for strategies to surmount present limitations in the development, evaluation and deployment of intelligent maritime systems that may accommodate future technological advances. Lessons learned that may be applied to improve safety of navigation for conventional shipping are also provided.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2019, 13, 3; 503-510
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Intelligent Prediction of Ship Maneuvering
    Autorzy:
    Łącki, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/116074.pdf
    Data publikacji:
    2016
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    future of navigation
    ship maneuvering
    intelligent prediction
    prediction of ship maneuvering
    neuroevolution
    navigational parameters
    intelligent ship
    autonomous control unit
    Opis:
    In this paper the author presents an idea of the intelligent ship maneuvering prediction system with the usage of neuroevolution. This may be also be seen as the ship handling system that simulates a learning process of an autonomous control unit, created with artificial neural network. The control unit observes input signals and calculates the values of required parameters of the vessel maneuvering in confined waters. In neuroevolution such units are treated as individuals in population of artificial neural networks, which through environmental sensing and evolutionary algorithms learn to perform given task efficiently. The main task of the system is to learn continuously and predict the values of a navigational parameters of the vessel after certain amount of time, regarding an influence of its environment. The result of a prediction may occur as a warning to navigator to aware him about incoming threat.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2016, 10, 3; 511-516
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Ship recognition and tracking system for intelligent ship based on deep learning framework
    Autorzy:
    Liu, B.
    Wang, S. Z.
    Xie, Z. X.
    Zhao, J. S.
    Li, M. F.
    Powiązania:
    https://bibliotekanauki.pl/articles/117419.pdf
    Data publikacji:
    2019
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    intelligent ship
    deep learning framework
    ship recognition system
    ship tracking system
    ship recognition and tracking system
    intelligent navigation
    autonomous ship
    maritime safety
    Opis:
    Automatically recognizing and tracking dynamic targets on the sea is an important task for intelligent navigation, which is the prerequisite and foundation of the realization of autonomous ships. Nowadays, the radar is a typical perception system which is used to detect targets, but the radar echo cannot depict the target’s shape and appearance, which affects the decision-making ability of the ship collision avoidance. Therefore, visual perception system based on camera video is very useful for further supporting the autonomous ship navigational system. However, ship’s recognition and tracking has been a challenge task in the navigational application field due to the long distance detection and the ship itself motion. An effective and stable approach is required to resolve this problem. In this paper, a novel ship recognition and tracking system is proposed by using the deep learning framework. In this framework, the deep residual network and cross-layer jump connection policy are employed to extract the advanced ship features which help enhance the classification accuracy, thus improves the performance of the object recognition. Experimentally, the superiority of the proposed ship recognition and tracking system was confirmed by comparing it with state of-the-art algorithms on a large number of ship video datasets.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2019, 13, 4; 699-705
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

      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