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Enhancing Marine Data Transmission with Socially-Aware Resilient Vessel Networks [article]

Ruobing Jiang, Chao Liu
2022 arXiv   pre-print
With the multi-dimensional exploration towards oceans, enormous sensing data has been generated with significant volume, velocity, variety and heterogeneity.  ...  In this paper, Resilient Vessel Network (RVN) is proposed to fundamentally enhance BMD transmission.  ...  The vessel trajectories are spatial-temporal positions of more than 12,000 fishing vessels from Jan. 2015 to Dec. 2018 by BeiDou Navigation Satellite System.  ... 
arXiv:2204.11654v1 fatcat:t7xphe6u7naxllphfm52x5uq6a

Remote Sensing in Vessel Detection and Navigation

Henning Heiselberg, Andrzej Stateczny
2020 Sensors  
The Special Issue (SI) "Remote Sensing in Vessel Detection and Navigation" highlighted a variety of topics related to remote sensing with navigational sensors.  ...  The 15 papers (from 23 submitted) were published.  ...  New techniques and methods for analyzing and extracting information from navigational sensors and data have been proposed and verified.  ... 
doi:10.3390/s20205841 pmid:33076456 fatcat:zn753a7d6vaybovgdfnsl3rlz4

A network abstraction of multi-vessel trajectory data for detecting anomalies

Iraklis Varlamis, Konstantinos Tserpes, Mohammad Etemad, Amílcar Soares Júnior, Stan Matwin
2019 Zenodo  
In this article, we present a methodology for creating a network abstraction of the trajectories of multiple vessels, which uses only the information collected from the vessels' Automatic Identification  ...  The resulting network abstraction contains rich information about the vessel behavior in an area and can be processed with network analysis and other data mining techniques in order to uncover hidden outliers  ...  ACKNOWLEDGEMENTS This work has been developed in the frame of the MASTER project, which has received funding from the European Union' s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie  ... 
doi:10.5281/zenodo.2649605 fatcat:cr3qfyyxtvfspmyk26md5syfse

Development and experimental study of analyzer to enhance maritime safety

Pavlo Nosov, Serhii Zinchenko, Viktor Plokhikh, Ihor Popovych, Yurii Prokopchuk, Dmytro Makarchuk, Pavlo Mamenko, Vladyslav Moiseienko, Andrii Ben
2021 Eastern-European Journal of Enterprise Technologies  
The use of automated artificial neural networks allowed defining critical situations in real time from the database of maritime transport management on the captain's bridge for an individual navigator.  ...  On the basis of empirical experimental data, relationships were identified indicating the influence of navigators' response to such vessel control indicators as maneuverability and safety.  ...  Acknowledgments The team of authors is grateful to the management of the Kherson State Maritime Academy (Ukraine) for the opportunity to conduct experiments using the certified Navi Trainer 5000 navigation  ... 
doi:10.15587/1729-4061.2021.239093 fatcat:le73aryps5gkva2br2apeoajo4

The Development of Key Technologies in Applications of Vessels Connected to the Internet

Zhe Tian, Fushun Liu, Zhixiong Li, Reza Malekian, Yingchun Xie
2017 Symmetry  
The purpose of this paper is to analyze how to benefit from the Internet of Vessels to improve the efficiency and safety of shipping, and promote the development of world transportation.  ...  Furthermore, the characteristics of the Internet of Vessels are described. Several important applications that illustrate the interaction of the Internet of Vessels' components are proposed.  ...  The purpose of this paper is to analyze how to benefit from the Internet of Vessels to create a more intelligent and safer navigating environment.  ... 
doi:10.3390/sym9100211 fatcat:oe5e2gqjfjdzxk7x63g3mwmkni

Exploiting AIS Data for Intelligent Maritime Navigation: A Comprehensive Survey [article]

Enmei Tu, Guanghao Zhang, Lily Rachmawati, Eshan Rajabally and Guang-Bin Huang
2016 arXiv   pre-print
The Automatic Identification System (AIS) tracks vessel movement by means of electronic exchange of navigation data between vessels, with onboard transceiver, terrestrial and/or satellite base stations  ...  This paper surveys AIS data sources and relevant aspects of navigation in which such data is or could be exploited for safety of seafaring, namely traffic anomaly detection, route estimation, collision  ...  When building a Bayesian network representation of vessel traffic, we can add a conditional dependence relation from the variable speed to ship type. (2) A fully defined Bayesian network is easily verified  ... 
arXiv:1606.00981v1 fatcat:azat2omu4za7zh4mfgyiw4bape

L-VTP: Long-Term Vessel Trajectory Prediction Based on Multi-Source Data Analysis

Chao Liu, Shuai Guo, Yuan Feng, Feng Hong, Haiguang Huang, Zhongwen Guo
2019 Sensors  
We have performed extensive experiments on two years of real-world trajectory data that include more than two thousand vessels.  ...  A traditional on-land trajectory prediction algorithm cannot be directly utilized in this field because trajectory characteristics of ocean vessels are far different from that on land.  ...  In order to build a low cost network to collect ocean data, researchers are devoted to constructing an optimal MDTN (Mobile Delay Tolerant Network) for ocean communication under a changeable and complicated  ... 
doi:10.3390/s19204365 fatcat:suudp3c6m5cuxlbkkjx5tpkm4u

Employing traditional machine learning algorithms for big data streams analysis: The case of object trajectory prediction

Angelos Valsamis, Konstantinos Tserpes, Dimitrios Zissis, Dimosthenis Anagnostopoulos, Theodora Varvarigou
2017 Journal of Systems and Software  
In this paper, we model the trajectory of sea vessels and provide a service that predicts in near-real time the position of any given vessel in 4', 10', 20' and 40' time intervals.  ...  We start with building models based on well-established machine learning algorithms using static datasets and multi-scan training approaches and identify the best candidate to be used in implementing a  ...  Our goal is to build an accurate model for predicting an object's trajectory; thus learning the object's kinematic equations.  ... 
doi:10.1016/j.jss.2016.06.016 fatcat:67vu77bmy5ekbdwla4dnumvdeu

Vehicle and Mission Control of the DELFIM Autonomous Surface Craft

J. Alves
2006 2006 14th Mediterranean Conference on Control and Automation  
DELFIM is an autonomous surface craft developed at ISR/IST for automatic marine data acquisition and to serve as an acoustic relay between submerged craft and a support vessel.  ...  The paper describes the navigation, guidance, and control systems of the vehicle, together with the mission control system that allows end-users to seamlessly program and run scientific missions at sea  ...  Navigation is done by integrating motion sensor data obtained from an attitude reference unit, a Doppler unit, and a DGPS (Differential Global Positioning System).  ... 
doi:10.1109/med.2006.236265 fatcat:3d7c6ts4sja4hfsgchig7pheq4

Vehicle and Mission Control of the DELFIM Autonomous Surface Craft

J. Alves, P. Oliveira, R. Oliveira, A. Pascoal, M. Rufino, L. Sebastiao, C. Silvestre
2006 2006 14th Mediterranean Conference on Control and Automation  
DELFIM is an autonomous surface craft developed at ISR/IST for automatic marine data acquisition and to serve as an acoustic relay between submerged craft and a support vessel.  ...  The paper describes the navigation, guidance, and control systems of the vehicle, together with the mission control system that allows end-users to seamlessly program and run scientific missions at sea  ...  Navigation is done by integrating motion sensor data obtained from an attitude reference unit, a Doppler unit, and a DGPS (Differential Global Positioning System).  ... 
doi:10.1109/med.2006.328689 fatcat:4dimh55xhbghbdwdmthire4pge

A Review over AI Methods Developed for Maritime Awareness Systems

Pohontu Alexandru
2020 Scientific Bulletin of Naval Academy  
Today's maritime surveillance and awareness systems can integrate multiple data sources like: coastal, HFSWR and SAR radars, AIS or satellite imagery; and this process produces massive amounts of data.  ...  That available data can be processed, with the use of Artificial Intelligence (AI) methods and algorithms, to automatically monitor the maritime traffic and its implications in safety, security, economy  ...  Vessels navigating in deep waters rarely perform manoeuvres in order to reduce ship trajectories into a sequence of waypoints [37] .  ... 
doi:10.21279/1454-864x-20-i2-107 fatcat:i4snqprjjbhoxlf7kdmiabfsk4

Uncovering Hidden Concepts from AIS Data: A Network Abstraction of Maritime Traffic for Anomaly Detection [chapter]

Ioannis Kontopoulos, Iraklis Varlamis, Konstantinos Tserpes
2020 Lecture Notes in Computer Science  
from the same vessel).  ...  The enriched network model can be processed and further examined with data mining techniques, even in an unsupervised manner, in order to identify anomalies in vessels' trajectories.  ...  This work has been developed in the frame of the MASTER and SmartShip projects, which have received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk lodowska-Curie  ... 
doi:10.1007/978-3-030-38081-6_2 fatcat:m7echmixirb6nlk7kwns3vewfy

Discovering Gateway Ports in Maritime Using Temporal Graph Neural Network Port Classification [article]

Dogan Altan, Mohammad Etemad, Dusica Marijan, Tetyana Kholodna
2022 arXiv   pre-print
The proposed method processes vessel trajectory data to build dynamic graphs capturing spatio-temporal dependencies between a set of static and dynamic navigational features in the data, and it is evaluated  ...  in terms of port classification accuracy on a real-world data set collected from ten vessels operating in Halifax, NS, Canada.  ...  Node extraction studies Vessel navigation movement is accessible in the form of a trajectory which is a series of consecutive spatio-temporal data.  ... 
arXiv:2204.11855v1 fatcat:kkcczas3rzelhgcwhldzpkt72e

Developing a Robotic Hybrid Network for Coastal Surveillance: the INFORE Experience

Gabriele FERRI, Raffaele GRASSO, Elena CAMOSSI, Alessandro FAGGIANI, Konstantina BERETA, Marios VODAS, Dimitris KLADIS, Dimitris ZISSIS, Kevin D. LePAGE
2022 Zenodo  
Data fusion from multi-modal data sources results crucial for the detection of complex events (e.g. illegal fishing), which can then be communicated to decision-makers.  ...  The framework enables the robots to make decisions on their navigation for improving vessel detection and tracking.  ...  The output of the fusion engine is a set of vessel trajectories deriving from multiple sources of maritime data as described above.  ... 
doi:10.5281/zenodo.6369005 fatcat:tfxcaxyufbeazmajbvem7wo3nm

Sensors and Sensor's Fusion in Autonomous Vehicles

Andrzej Stateczny, Marta Wlodarczyk-Sielicka, Pawel Burdziakowski
2021 Sensors  
Autonomous vehicle navigation has been at the center of several major developments, both in civilian and defense applications [...]  ...  [3] presented a unique combination of bathymetric data obtained from an unmanned surface vessel, photogrammetric data obtained from unmanned aerial vehicles and ground laser scanning, and geodetic data  ...  Another example of data acquired from modern systems and sensors mounted on autonomous vehicles is bathymetry data. These data are depth points acquired from a multi-beam echo sounder.  ... 
doi:10.3390/s21196586 pmid:34640906 fatcat:l46q6lgphbgw7clcd4d7vtksuu
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