scholarly journals Predicting Ship Trajectory Based on Neural Networks Using AIS Data

2021 ◽  
Vol 9 (3) ◽  
pp. 254
Author(s):  
Tamara A. Volkova ◽  
Yulia E. Balykina ◽  
Alexander Bespalov
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Navigation System ◽  
Autonomous Navigation ◽  
Hydraulic Structures ◽  
Automatic Mode ◽  
Hydrographic Conditions ◽  
Satellite Signal ◽  
The Moment ◽  
Actual Trajectory

To create an autonomously moving vessel, it is necessary to know exactly how to determine the current coordinates of the vessel in the selected coordinate system, determine the actual trajectory of the vessel, estimate the motion trend to predict the current coordinates, and calculate the course correction to return to the line of the specified path. The navigational and hydrographic conditions of navigation on each section of the route determine the requirements for the accuracy of observations and the time spent on locating the vessel. The problem of predicting the trajectory of the vessel's motion in automatic mode is especially important for river vessels or river-sea vessels, predicting the trajectory of the route sections during the maneuvering of the vessel. At the moment, one of the most accurate ways of determining the coordinates of the vessel is by reading the satellite signal. However, when a vessel is near hydraulic structures, problems may arise connected with obtaining a satellite signal due to interference and, therefore, the error in measuring the coordinates of the vessel increases. The likelihood of collisions and various kinds of incidents increases. In such cases, it is possible to correct the trajectory of the movement using an autonomous navigation system. In this work, opportunities of the possible application of artificial neural networks to create such a corrective system using only the coordinates of the ship's position are discussed. It was found that this is possible on sections of the route where the ship does not maneuver.

Artificial neural networks in the problem of determining the position and size of the solar disk in wide-angle photographs of the sky

2021 ◽  
Author(s):  
Mikhail Borisov ◽  
Mikhail Krinitskiy
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Disk ◽  
Weather Conditions ◽  
Wide Angle ◽  
Know How ◽  
The Neural Network ◽  
Artificial Neural ◽  
The Moment

<p>Total cloud score is a characteristic of weather conditions. At the moment, there are algorithms that automatically calculate cloudiness based on a photograph of the sky These algorithms do not know how to find the solar disk, so their work is not absolutely accurate.</p><p>To create an algorithm that solves this data, the data used, obtained as a result of sea research voyages, is used, which is marked up for training the neural network.</p><p>As a result of the work, an algorithm was obtained based on neural networks, based on a photograph of the sky, in order to determine the size and position of the solar disk, other algorithms can be used to work with images of the visible hemisphere of the sky.</p>

scholarly journals Efficient Training of Artificial Neural Networks for Autonomous Navigation

1991 ◽  
Vol 3 (1) ◽  
pp. 88-97 ◽  
Author(s):  
Dean A. Pomerleau
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Autonomous Navigation ◽  
Training Techniques ◽  
Human Driver ◽  
Backpropagation Network ◽  
Artificial Neural ◽  
Network Project

The ALVINN (Autonomous Land Vehicle In a Neural Network) project addresses the problem of training artificial neural networks in real time to perform difficult perception tasks. ALVINN is a backpropagation network designed to drive the CMU Navlab, a modified Chevy van. This paper describes the training techniques that allow ALVINN to learn in under 5 minutes to autonomously control the Navlab by watching the reactions of a human driver. Using these techniques, ALVINN has been trained to drive in a variety of circumstances including single-lane paved and unpaved roads, and multilane lined and unlined roads, at speeds of up to 20 miles per hour.

TIME SERIES FORECASTING. A COMPARATIVE STUDY BETWEEN AN EVOLVING ARTIFICIAL NEURAL NETWORKS SYSTEM AND STATISTICAL METHODS

2012 ◽  
Vol 21 (01) ◽  
pp. 1250010 ◽  
Author(s):  
JUAN PERALTA DONATE ◽  
GERMAN GUTIERREZ SANCHEZ ◽  
ARACELI SANCHIS DE MIGUEL
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Fitness Function ◽  
Time Series Forecasting ◽  
Automatic Process ◽  
Global Learning ◽  
Unobserved Components Model ◽  
Artificial Neural ◽  
The Moment

Accurate time series forecasting are important for displaying the manner in which the past continues to affect the future and for planning our day to-day activities. In recent years, a large literature has evolved on the use of evolving artificial neural networks (EANN) in many forecasting applications. Evolving neural networks are particularly appealing because of their ability to model an unspecified non-linear relationship between time series variables. In this work, a new approach of a previous Automatic Design of Artificial Neural Networks (ADANN) system applied to forecast time series is tackled. The automatic process to design artificial neural networks is carried out by a genetic algorithm (GA). These new methods, in order to get an accurate forecasting, are related with: shuffling training and validation patterns obtained from time series values and trying to improve the fitness function used in the global learning process (i.e. GA) using a new patterns set called validation II apart of the two used till the moment (i.e. training and validation). The object of this study is to try to improve the final forecasting getting an accurate system. In this paper, we also compare the forecasting ability of the ARIMA approach, evolving artificial neural networks (ADANN), unobserved components model (UCM) and a forecasting tool called Forecast Pro software using six benchmark time series.

Using Artificial Neural Networks to Compensate for the Error in an Integrated Navigation System

2020 ◽  
pp. 4-26
Author(s):  
N. Al Bitar ◽  
A.I. Gavrilov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Navigation System ◽  
Satellite Navigation ◽  
Satellite System ◽  
Integrated Navigation ◽  
Satellite Navigation System ◽  
Integrated Navigation System ◽  
Artificial Neural

The paper presents a new method for improving the accuracy of an integrated navigation system in terms of coordinate and velocity when there is no signal received from the global navigation satellite system. We used artificial neural networks to simulate the error occurring in an integrated navigation system in the absence of the satellite navigation system signal. We propose a method for selecting the inputs for the artificial neural networks based on the mutual information (MI) criterion and lag-space estimation. The artificial neural network employed is a non-linear autoregressive neural network with external inputs. We estimated the efficiency of using our method to solve the problem of compensating for the error in an integrated navigation system in the absence of the satellite navigation system signal

scholarly journals Forecasting Parameters of Satellite Navigation Signal through Artificial Neural Networks for the Purpose of Civil Aviation

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Karolina Krzykowska ◽  
Michał Krzykowski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Satellite Navigation ◽  
Civil Aviation ◽  
Artificial Satellites ◽  
Navigation Systems ◽  
Atmospheric Conditions ◽  
Navigation Signal ◽  
Satellite Signal ◽  
Artificial Neural

Navigation is a key element influencing fluent, rapid, and safe transport of people and goods. During the last years, special attention was paid to satellite navigation, which is a part of radionavigation where positioning is done thanks to artificial satellites. Issues of application and development of satellite navigation systems in civil aviation are the subject of numerous research and scientific studies in the world. The quality of satellite signal determined by parameters such as accuracy, continuity, availability, and integrity determines possibility of its operational use. Particular attention of scientific research is therefore devoted to the requirements and limitations imposed on satellite systems prior to their implementation in aviation. This extremely important aspect justified undertaking of the aforementioned problem in this article. The paper attempts to answer the question on how to facilitate selection of navigation techniques for the aircraft operator, taking into account factors determining the accuracy, continuity, availability, and integrity of the satellite signal. As a result, the purpose of the work was defined as development of a method for forecasting the values of satellite navigation signal parameters used in air transport by artificial neural networks, taking into account selected atmospheric conditions. Results included in the work indicate further directions of satellite navigation system development. Due to authors’ opinion, the researches should focus especially on the analysis of real-time satellite signal parameter performance or creating applications for UAVs automatically deciding about used techniques of navigation.

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