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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:
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:
Rainfall-river discharge modelling for flood forecasting using Artificial Neural Network (ANN)
Autorzy:
Obasi, Arinze A.
Ogbu, Kingsley N.
Orakwe, Chukwuemeka L.
Ahaneku, Isiguzo E.
Powiązania:
https://bibliotekanauki.pl/articles/292776.pdf
Data publikacji:
2020
Wydawca:
Instytut Technologiczno-Przyrodniczy
Tematy:
artificial neural network (ANN)
rainfall
flood forecasting
river discharge
Opis:
This study is aimed at evaluating the applicability of Artificial Neural Network (ANN) model technique for river discharge forecasting. Feed-forward multilayer perceptron neural network trained with back-propagation algorithm was employed for model development. Hydro-meteorological data for the Imo River watershed, that was collected from the Anambra-Imo River Basin Development Authority, Owerri – Imo State, South-East, Nigeria, was used to train, validate and test the model. Coefficients of determination results are 0.91, 0.91 and 0.93 for training, validation and testing periods respectively. River discharge forecasts were fitted against actual discharge data for one to five lead days. Model results gave R2 values of 0.95, 0.95, 0.92, 0.96 and 0.94 for first, second, third, fourth, and fifth lead days of forecasts, respectively. It was generally observed that the R2 values decreased with increase in lead days for the model. Generally, this technique proved to be effective in river discharge modelling for flood forecasting for shorter lead-day times, especially in areas with limited data sets.
Źródło:
Journal of Water and Land Development; 2020, 44; 98-105
1429-7426
2083-4535
Pojawia się w:
Journal of Water and Land Development
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
“P” coordinator scheme and interaction prediction principle in hierarchical structure of ANN
Autorzy:
Płaczek, S.
Powiązania:
https://bibliotekanauki.pl/articles/97277.pdf
Data publikacji:
2015
Wydawca:
Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Tematy:
Artificial Neural Network (ANN)
hierarchy
decomposition
coordination
coordination principle
P-regulator
feedback principle
Opis:
When implementing the hierarchical structure [4][5] of the learning algorithm of an Artificial Neural Network (ANN), two very important questions have to be solved. The first one is connected with the selection of the broad coordination principle. In [1], three different principles are described. They vary with regard to the degree of freedom for the first-level tasks. The second problem is connected with the coordinator structure or, in other words, the coordination algorithm. In the regulation theory, the process of finding the coordinator structure is known as the feedback principle. The simplest regulator structure (scheme) is known as the proportional regulator – “P” regulator. In the article, the regulator structure and its parameters are analysed as well as their impact on the learning process quality.
Źródło:
Computer Applications in Electrical Engineering; 2015, 13; 319-329
1508-4248
Pojawia się w:
Computer Applications in Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Experimental and numerical investigation of the deep drawing process for an automobile panel and prediction of appropriate amount of parameters by multi-layer neural network
Autorzy:
Najafabadi, S. S.
Anaraki, A. T.
Moradi, M.
Powiązania:
https://bibliotekanauki.pl/articles/281868.pdf
Data publikacji:
2017
Wydawca:
Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Tematy:
deep drawing
finite element analysis (FEA)
multi-layer artificial neural network (ANN)
Taguchi design
Opis:
In this paper, the deep drawing process of an automobile panel in order to select the appropriate amount of parameters has been investigated. The parameters include friction between the blank and die, blank width and length, blank thickness and gap between the blank and blank-holder. A multi-layer artificial neural network (ANN) trained by finite element analyses (FEA) is applied in order to improve forming parameters and achieve a better quality. As the FEA results are used to train the ANN, the FEA results have been verified by three experiments. Finally, an appropriate amount of each parameter is predicted by the trained ANN and a FEA has been done based on the ANN prediction to evaluate the accuracy of the trained ANN. Moreover, it is shown that the ANN could predict results within a 10 percent error. In addition, the proposed method for prediction of the appropriate parameters (ANN) is confirmed by comparing with the Taguchi design of experiment prediction. It is also shown that the model obtained by the former method has lower errors than the latter one. In this study, the Taguchi model is used to evaluate the effect of parameters on tearing and wrinkling. Based on the Taguchi design of experiment, while the blank length is the most effective parameter on tearing, the maximum height of wrinkles on flanged parts mainly depends on the blank thickness.
Źródło:
Journal of Theoretical and Applied Mechanics; 2017, 55, 2; 707-718
1429-2955
Pojawia się w:
Journal of Theoretical and Applied Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparative study of learning methods for artificial neural network
Badania porównawcze metod uczenia sieci neuronowej
Autorzy:
Tiliouine, H.
Powiązania:
https://bibliotekanauki.pl/articles/153863.pdf
Data publikacji:
2007
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
metody uczenia
sieć neuronowa
neuronowy regulator napięcia
learning methods
artificial neural network (ANN)
neural voltage controller
Opis:
The paper presents a comparative study of various learning methods for artificial neural network. The methods are: the backpropagation BP, the recursive least squares RLS, the Zangwill's method ZGW and the method based on evolutionary algorithm EA. The study consists of evaluating the learning effectiveness of these methods and selecting the most efficient one to be used in the designing of an adaptive neural voltage controller for a synchronous generator.
W artykule przedstawiono wyniki badań porównawczych metod uczenia sieci neuronowych takich jak: metoda propagacji wstecznej błędów, rekurencyjna metoda najmniejszych kwadratów, metoda Zangwill'a, metoda algorytmów ewolucyjnych. Celem tych badań jest dobieranie najefektywniejszej metody uczenia do projektowania adaptacyjnego neuronowego regulatora napięcia generatora synchronicznego.
Źródło:
Pomiary Automatyka Kontrola; 2007, R. 53, nr 4, 4; 117-121
0032-4140
Pojawia się w:
Pomiary Automatyka Kontrola
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Dynamically-adaptive Weight in Batch Back Propagation Algorithm via Dynamic Training Rate for Speedup and Accuracy Training
Autorzy:
Al_Duais, M. S.
Mohamad, F. S.
Powiązania:
https://bibliotekanauki.pl/articles/307920.pdf
Data publikacji:
2017
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
artificial neural network (ANN)
batch back propagation algorithm
dynamic training rate
speed up training
accuracy training
Opis:
The main problem of batch back propagation (BBP) algorithm is slow training and there are several parameters need to be adjusted manually, such as learning rate. In addition, the BBP algorithm suffers from saturation training. The objective of this study is to improve the speed up training of the BBP algorithm and to remove the saturation training. The training rate is the most significant parameter for increasing the efficiency of the BBP. In this study, a new dynamic training rate is created to speed the training of the BBP algorithm. The dynamic batch back propagation (DBBPLR) algorithm is presented, which trains with adynamic training rate. This technique was implemented with a sigmoid function. Several data sets were used as benchmarks for testing the effects of the created dynamic training rate that we created. All the experiments were performed on Matlab. From the experimental results, the DBBPLR algorithm provides superior performance in terms of training, faster training with higher accuracy compared to the BBP algorithm and existing works.
Źródło:
Journal of Telecommunications and Information Technology; 2017, 4; 82-89
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Efficient dead time correction of G-M counters using feed forward artificial neural network
Autorzy:
Arkani, M.
Khalafi, A.
Powiązania:
https://bibliotekanauki.pl/articles/146121.pdf
Data publikacji:
2013
Wydawca:
Instytut Chemii i Techniki Jądrowej
Tematy:
dead time
artificial neural network (ANN)
Geiger-Müller (G-M) detector
hybrid model
source decaying experiment
Opis:
Dead time parameter of Geiger-Müller (G-M) counters causes a great uncertainty in their response to the incident radiation intensity at high counting rates. As their applications in experimental nuclear science are widespread, many attempts have been done on improvements of their nonlinear response. In this work, response of a G-M counter system is optimized and corrected efficiently using feed forward artificial neural network (ANN). This method is simple, fast, and provides the answer to the problem explicitly with no need for iteration. The method is applied to a set of decaying source experimental data measured by a fairly large G-M tube. The results are compared with those predicted by a given analytical model which is called hybrid model. The maximum deviation of the corrected results from the true counting rates is less than 4% which is a significant improvement in comparison with the results obtained by the analytical method. Results of this study show that by using a proper artificial neural network structure, the dead time effects of G-M counters can be tolerated significantly.
Źródło:
Nukleonika; 2013, 58, 2; 317-321
0029-5922
1508-5791
Pojawia się w:
Nukleonika
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Resistance Prediction for Hard Chine Hulls in the Pre-Planing Regime
Autorzy:
Radojcic, D.
Zgradic, A.
Kalajdzic, M.
Simic, A.
Powiązania:
https://bibliotekanauki.pl/articles/259047.pdf
Data publikacji:
2014
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
planing craft
hard chine hulls
resistance evaluation
Artificial Neural Network (ANN)
TUNS Series
USCG Series
pre-planing regime
Opis:
A mathematical representation of calm-water resistance for contemporary planing hull forms based on the USCG and TUNS Series is presented. Regression analysis and artificial neural network (ANN) techniques are used to establish, respectively, Simple and Complex mathematical models. For the Simple model, resistance is the dependent variable (actually R/Δ for standard displacement of Δ = 100000 lb), while the Froude number based on volume (FnV) and slenderness ration (L/V1/3) are the independent variables. In addition to these, Complex model’s independent variables are the length beam ratio (L/B), the position of longitudinal centre of gravity (LCG/L) and the deadrise angle (β). The speed range corresponding to FnV values between 0.6 and 3.5 is analyzed. The Simple model can be used in the concept design phases, while the Complex one might be used for various numerical towing tank performance predictions during all design phases, as appropriate.
Źródło:
Polish Maritime Research; 2014, 2; 9-26
1233-2585
Pojawia się w:
Polish Maritime Research
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:
Investigation Of Infrared Drying Behaviour Of Spinach Leaves Using ANN Methodology And Dried Product Quality
Autorzy:
Sarimeseli, A.
Yuceer, M.
Powiązania:
https://bibliotekanauki.pl/articles/185285.pdf
Data publikacji:
2015
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Artificial neural network (ANN)
infrared
spinach drying
ascorbic acid
rehydration
colour parameters
sztuczne sieci neuronowe
suszenie
kwas askorbinowy
rehydracja
Opis:
Effects of infrared power output and sample mass on drying behaviour, colour parameters, ascorbic acid degradation, rehydration characteristics and some sensory scores of spinach leaves were investigated. Within both of the range of the infrared power outputs, 300–500 W, and sample amounts, 15–60 g, moisture content of the leaves was reduced from 6.0 to 0.1±(0.01) kg water/ kg dry base value. It was recorded that drying times of the spinach leaves varied between 3.5–10 min for constant sample amount, and 4–16.5 min for constant power output. Experimental drying data obtained were successfully investigated by using artificial neural network methodology. Some changes were recorded in the quality parameters of the dried leaves, and acceptable sensory scores for the dried leaves were observed in all of the experimental conditions.
Źródło:
Chemical and Process Engineering; 2015, 36, 4; 425-436
0208-6425
2300-1925
Pojawia się w:
Chemical and Process Engineering
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:
Safe vibrations of spilling basin explosions at "Gotvand Olya Dam" using artificial neural network
Określanie bezpiecznego poziomu wibracji w zbiorniku w trakcie prac strzałowych prowadzonych na tamie Gotvand Olya z wykorzystaniem sztucznych sieci neuronowych
Autorzy:
Amnieh, H. B.
Bahadori, M.
Powiązania:
https://bibliotekanauki.pl/articles/219884.pdf
Data publikacji:
2014
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
drgania gruntu
bezpieczeństwo prac strzałowych
tama Gotvand Olya
sztuczne sieci neuronowe
ground vibration
safe explosion
Gotvand Olya Dam
artificial neural network (ANN)
Opis:
Ground vibration is an undesirable outcome of an explosion which can have destructive effects on the surrounding environment and structures. Peak Particle Velocity (PPV) is a determining factor in evaluation of the damage caused by an explosion. To predict the ground vibration caused by blasting at the Gotvand Olya Dam (GOD) spilling basin, thirty 3-component records (totally 90) from 19 blasts were obtained using 3 VIBROLOC seismographs. Minimum and the maximum distance from the center of the exploding block to the recording station were set to be 11 and 244 meters, respectively. To evaluate allowable safe vibration and determining the permissible explosive charge weight, Artificial Neural Networks (ANN) was employed with Back Propagation (BP) and 3 hidden layers. The mean square error and the correlation coefficient of the network in this study were found to be 1.95 and 0.995, respectively, which compared to those obtained from the known empirical correlations, indicating substantially more accurate prediction. Considering the network high accuracy and precision in predicting vibrations caused by such blasting operations, the nearest distance from the center of the exploding block at this study was 11 m, and considering the standard allowable vibration of 120 mm/sec for heavy concrete structures, the maximum permissible explosive weight per delay was estimated to be 47.00 Kg. These results could be employed in subsequent safer blasting operation designs.
Wibracje gruntu to niepożądany skutek prowadzenia prac strzałowych, które mogą negatywnie wpływać na otaczające środowisko oraz znajdujące się w sąsiedztwie budowle. Głównym wskaźnikiem używanym przy określaniu szkód spowodowanych przez wybuchy jest wskaźnik maksymalnej prędkości cząstek (PPV). Przy prognozowaniu wibracji terenu wskutek prac strzałowych prowadzonych na tamie Gotvand Olya i w zbiorniku zbadano zapisy 3-składnikowych prędkości ( w sumie 90 zapisów) z 13 wybuchów zarejestrowane przy użyciu sejsmografu 3 VIBROLOC. Maksymalna i minimalna odległość pomiędzy środkiem rozkruszanego bloku a stacją rejestrującą ustawiona została na poziomie 244 i 11 m. W celu określenia bezpiecznego poziomu drgań oraz dopuszczalnej wagi ładunku, zastosowano podejście wykorzystujące sieci neuronowe, z wykorzystaniem metody propagacji wstecznej i trzech warstw ukrytych. Błąd średniokwadratowy i współczynnik korelacji sieci wyniosły 1.95 i 0.95, co pozostaje w zgodności z danym uzyskiwanymi z obserwacji empirycznych, wskazując na poprawność i dokładność prognoz. Zakładając wysoki poziom dokładności sieci oraz wysoką dokładność w prognozowaniu poziomu drgań wywołanych przez prace strzałowe, przyjęto że najbliższa odległość od środka rozkruszanego bloku wyniesie 11 m. Uwzględniając standardowe dopuszczalne w przypadku ciężkich budowli betonowych poziomy drgań w wysokości 120 m/s, oszacowano że maksymalna dopuszczalna masa ładunku wyniesie 47.00 Kg, w przeliczeniu na jeden okres zwłoki. Wyniki badań wykorzystane być mogą w planowaniu kolejnych bezpiecznych prac strzałowych.
Źródło:
Archives of Mining Sciences; 2014, 59, 4; 1087-1096
0860-7001
Pojawia się w:
Archives of Mining Sciences
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
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