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 "artificial network" wg kryterium: Temat

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-11 z 11
    Tytuł:
    Application of Artificial Neural Network into the Water Level Modeling and Forecast
    Autorzy:
    Sztobryn, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/116204.pdf
    Data publikacji:
    2013
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    artificial neural network (ANN)
    hydrography
    coastal area
    Opis:
    The dangerous sea and river water level increase does not only destroy the human lives, but also generate the severe flooding in coastal areas. The rapidly changes in the direction and velocity of wind and associated with them sea level changes could be the severe threat for navigation, especially on the fairways of small fishery harbors located in the river mouth. There is the area of activity of two external forcing: storm surges and flood wave. The aim of the work was the description of an application of Artificial Neural Network (ANN) methodology into the water level forecast in the case study field in Swibno harbor located is located at 938.7 km of the Wisla River and at a distance of about 3 km up the mouth (Gulf of Gdansk ‐ Baltic Sea).
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2013, 7, 2; 219-223
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Gap Filling of Daily Sea Levels by Artificial Neural Networks
    Autorzy:
    Pashova, L.
    Koprinkova-Hristova, P. D.
    Popova, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/116147.pdf
    Data publikacji:
    2013
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    artificial neural network (ANN)
    hydrography
    Black Sea
    Opis:
    In the recent years, intelligent methods as artificial neural networks are successfully applied for data analysis from different fields of the geosciences. One of the encountered practical problems is the availability of gaps in the time series that prevent their comprehensive usage for the scientific and practical purposes. The article briefly describes two types of the artificial neural network (ANN) architectures ‐ Feed‐ Forward Backpropagation (FFBP) and recurrent Echo state network (ESN). In some cases, the ANN can be used as an alternative on the traditional methods, to fill in missing values in the time series. We have been conducted several experiments to fill the missing values of daily sea levels spanning a 5‐years period using both ANN architectures. A multiple linear regression for the same purpose has been also applied. The sea level data are derived from the records of the tide gauge Burgas, which is located on the western Black Sea coast. The achieved results have shown that the performance of ANN models is better than that of the classical one and they are very promising for the real‐time interpolation of missing data in the time series.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2013, 7, 2; 225-232
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Modern method based on artificial intelligence for safe control in the marine environment
    Autorzy:
    Mohamed-Seghir, Gdynia Maritime University, Gdynia, Poland
    Powiązania:
    https://bibliotekanauki.pl/articles/24201419.pdf
    Data publikacji:
    2023
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    safe ship's trajectory
    collision avoidance
    artificial intelligence
    artificial neural network
    fuzzy logic
    evolutionary algorithms
    dynamic programming
    Opis:
    The article presents an approach to formulating a ship control process model in order to solve the problem of determining a safe ship trajectory in collision situations. Fuzzy process properties are included in the model to bring it closer to reality, as in many situations the navigator makes a subjective decision. A special neural network was used to solve the presented problem. This artificial neural network is characterized by minimum and maximum operations when set. In order to confirm the correctness of the operation of the proposed algorithm, the results of the simulations obtained were presented and an discussion was conducted.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2023, 17, 2; 283--288
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The Development of a combined method to quicklyassess ship speed and fuel consumption at different powertrain load and sea conditions
    Autorzy:
    Krata, P.
    Kniat, A.
    Guedes Soares, C.
    Krata, H.
    Powiązania:
    https://bibliotekanauki.pl/articles/1841479.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    combined method
    sea conditions
    ship speed
    fuel consumption
    powertrain load
    Artificial Neural Network
    Opis:
    Decision support systems (DSS) recently have been increasingly in use during ships operation. They require realistic input data regarding different aspects of navigation. To address the optimal weather routing of a ship, which is one of the most promising field of DSS application, it is necessary to accurately predict an actually attainable speed of a ship and corresponding fuel consumption at given loading conditions and predicted weather conditions. In this paper, authors present a combined calculation method to predict those values. First, a deterministic modeling is applied and then an artificial neural network (ANN) is structured and trained to quickly mimic the calculations. The sensitivity of the ANN to adopted settings is analyzed as well. The research results confirm a more than satisfactory quality of reproduction of speed and fuel consumption data as the ANN response meet the calculation results with high accuracy. The ANN-based approach, however, requires a significantly shorter time of execution. The directions of future research are outlined.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2021, 15, 2; 437-445
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Neuroevolutionary approach to COLREGs ship maneuvers
    Autorzy:
    Łącki, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/116206.pdf
    Data publikacji:
    2019
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    collision avoidance
    colregs
    neuroevolutionary approach to colregs
    ship handling system
    artificial helmsman
    Artificial Neural Network (ANN)
    evolutionary algorithms
    ship manoeuvering
    Opis:
    The paper describes the usage of neuroevolutionary method in collision avoidance of two power-driven vessels approaching each other regarding COLREGs rules. This may be also be seen as the ship handling system that simulates a learning process of a group of artificial helmsmen - autonomous control units, created with artificial neural networks. The helmsman observes an environment by its input signals and according to assigned CORLEGs rule, he calculates the values of required parameters of maneuvers (propellers rpm and rudder deflection) in a collision avoidance situation. 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 safely and efficiently. The main task of this project is to evolve a population of helmsmen which is able to effectively implement chosen rule: crossing or overtaking.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2019, 13, 4; 745-750
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application perspective of digitalneural networks in the context of marine technologies
    Autorzy:
    Konon, V.
    Konon, N.
    Powiązania:
    https://bibliotekanauki.pl/articles/24201415.pdf
    Data publikacji:
    2022
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    marine technology
    multi-layer perceptron
    neural networks
    digital neural networks
    maritime industry
    MLP algorithm
    3D model
    Artificial Neural Network
    Opis:
    This study is focused on the issue of digital neural networks’ implementation in the context of maritime industry. Various algorithms of such networks in the terms of the marine technologies have been reviewed in the current study in order to evaluate the effectiveness of the methodology and to propose a new concept of an artificial neural network’s application in this way. Fire-detection system simulation based on the thermal imagers’ data input had been developed to assess the efficiency of the concept suggested with a multi-layer perceptron (MLP) algorithm integrated into the designed 3d-model.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2022, 16, 4; 743--747
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Reinforcement Learning in Ship Handling
    Autorzy:
    Łącki, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/117361.pdf
    Data publikacji:
    2008
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    Ship Handling
    Reinforcement Learning
    Machine Learning Techniques
    Manoeuvring
    Restricted Waters
    Markov Decision Process (MDP)
    Artificial Neural Network (ANN)
    multi-agent environment
    Opis:
    This paper presents the idea of using machine learning techniques to simulate and demonstrate learning behaviour in ship manoeuvring. Simulated model of ship is treated as an agent, which through environmental sensing learns itself to navigate through restricted waters selecting an optimum trajectory. Learning phase of the task is to observe current state and choose one of the available actions. The agent gets positive reward for reaching destination and negative reward for hitting an obstacle. Few reinforcement learning algorithms are considered. Experimental results based on simulation program are presented for different layouts of possible routes within restricted area.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2008, 2, 2; 157-160
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Analysis of Methods of Determining the Safe Ship Trajectory
    Autorzy:
    Lisowski, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/116647.pdf
    Data publikacji:
    2016
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    ship route
    Safe Ship Trajectory
    route planning
    Determining the Safe Ship Trajectory
    ship trajectory
    Artificial Neural Network (ANN)
    game theory
    computer simulation
    Opis:
    The paper describes six methods of optimal and game theory and artificial neural network for synthesis of safe control in collision situations at sea. The application of optimal and game control algorithms to determine the own ship safe trajectory during the passing of other encountered ships in good and restricted visibility at sea is presented. The comparison of the safe ship control in collision situation: multi-step matrix non-cooperative and cooperative games, multi-stage positional non-cooperative and cooperative games have been introduced. The considerations have been illustrated with examples of computer simulation of the algorithms to determine safe of own ship trajectories in a navigational situation during passing of eight met ships.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2016, 10, 2; 223-228
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Effect of ship neural domain shape on safe and optimal trajectory
    Autorzy:
    Lisowski, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/24201475.pdf
    Data publikacji:
    2023
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    artificial neural network model
    method for optimization
    dynamic programming method
    ship safety domain
    safe ship control
    path planning
    multi-object decision model
    computer simulation
    Opis:
    This article presents the task of safely guiding a ship, taking into account the movement of many other marine units. An optimally neural modified algorithm for determining a safe trajectory is presented. The possible shapes of the domains assigned to other ships as traffic restrictions for the particular ship were subjected to a detailed analysis. The codes for the computer program Neuro-Constraints for generating these domains are presented. The results of the simulation tests of the algorithm for a navigational situation are presented. The safe trajectories of the ship were compared at different distances, changing the sailing conditions and ship sizes.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2023, 17, 1; 185--191
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Consistently Trained Artificial Neural Network for Automatic Ship Berthing Control
    Autorzy:
    Ahmed, Y.
    Hasegawa, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/116809.pdf
    Data publikacji:
    2015
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    Port Maneuvres
    Artificial Neural Network (ANN)
    Automatic Ship Berthing Control
    Ship Berthing
    Automatic Ship Berthing
    Monte Carlo simulation
    Autonomous Underwater Vehicle (AUV)
    Teaching Data Creation
    Opis:
    In this paper, consistently trained Artificial Neural Network controller for automatic ship berthing is discussed. Minimum time course changing manoeuvre is utilised to ensure such consistency and a new concept named ‘virtual window’ is introduced. Such consistent teaching data are then used to train two separate multi-layered feed forward neural networks for command rudder and propeller revolution output. After proper training, several known and unknown conditions are tested to judge the effectiveness of the proposed controller using Monte Carlo simulations. After getting acceptable percentages of success, the trained networks are implemented for the free running experiment system to judge the network’s real time response for Esso Osaka 3-m model ship. The network’s behaviour during such experiments is also investigated for possible effect of initial conditions as well as wind disturbances. Moreover, since the final goal point of the proposed controller is set at some distance from the actual pier to ensure safety, therefore a study on automatic tug assistance is also discussed for the final alignment of the ship with actual pier.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2015, 9, 3; 417-426
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Small Wind Turbine Output Model for Spatially Constrained Remote Island Micro-Grids
    Autorzy:
    Žigman, D.
    Meštrović, K.
    Tomiša, T.
    Powiązania:
    https://bibliotekanauki.pl/articles/2172468.pdf
    Data publikacji:
    2022
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    wind turbine
    small wind turbine
    decision tree model
    artificial neural network model
    random forest model
    micro-grids
    spatially constrained remote Island micro-grids
    remote Island micro-grid
    Opis:
    Modelling operation of the power supply system for remote island communities is essential for its operation, as well as a survival of a modern society settled in challenging conditions. Micro-grid emerges as a proper solution for a sustainable development of a spatially constrained remote island community, while at the same time reflecting the power requirements of similar maritime subjects, such as large vessels and fleets. Here we present research results in predictive modelling the output of a small wind turbine, as a component of a remote island micro-grid. Based on a month-long experimental data and the machine learning-based predictive model development approach, three candidate models of a small wind turbine output were developed, and assessed on their performance based on an independent set of experimental data. The Random Forest Model out performed competitors (Decision Tree Model and Artificial Neural Network Model), emerging as a candidate methodology for the all-year predictive model development, as a later component of the over-all remote island micro-grid model.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2022, 16, 1; 143--146
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-11 z 11

      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