Littérature scientifique sur le sujet « Maritime anomaly detection »
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Articles de revues sur le sujet "Maritime anomaly detection"
Iphar, Clément, Cyril Ray et Aldo Napoli. « Data integrity assessment for maritime anomaly detection ». Expert Systems with Applications 147 (juin 2020) : 113219. http://dx.doi.org/10.1016/j.eswa.2020.113219.
Texte intégralKim, Donghyun, Gian Antariksa, Melia Putri Handayani, Sangbong Lee et Jihwan Lee. « Explainable Anomaly Detection Framework for Maritime Main Engine Sensor Data ». Sensors 21, no 15 (31 juillet 2021) : 5200. http://dx.doi.org/10.3390/s21155200.
Texte intégralTserpes, Konstantinos, Konstantinos Chatzikokolakis, Dimitris Zissis, Giannis Spiliopoulos et Ioannis Kontopoulos. « Real-time maritime anomaly detection : detecting intentional AIS switch-off ». International Journal of Big Data Intelligence 7, no 2 (2020) : 85. http://dx.doi.org/10.1504/ijbdi.2020.10029526.
Texte intégralKontopoulos, Ioannis, Konstantinos Chatzikokolakis, Dimitris Zissis, Konstantinos Tserpes et Giannis Spiliopoulos. « Real-time maritime anomaly detection : detecting intentional AIS switch-off ». International Journal of Big Data Intelligence 7, no 2 (2020) : 85. http://dx.doi.org/10.1504/ijbdi.2020.107375.
Texte intégralSithiravel, Rajiv, Bhashyam Balaji, Bradley Nelson, Michael Kenneth McDonald, Ratnasingham Tharmarasa et Thiagalingam Kirubarajan. « Airborne Maritime Surveillance Using Magnetic Anomaly Detection Signature ». IEEE Transactions on Aerospace and Electronic Systems 56, no 5 (octobre 2020) : 3476–90. http://dx.doi.org/10.1109/taes.2020.2973866.
Texte intégralKazemi, Samira, Shahrooz Abghari, Niklas Lavesson, Henric Johnson et Peter Ryman. « Open data for anomaly detection in maritime surveillance ». Expert Systems with Applications 40, no 14 (octobre 2013) : 5719–29. http://dx.doi.org/10.1016/j.eswa.2013.04.029.
Texte intégralHan, X., C. Armenakis et M. Jadidi. « DBSCAN OPTIMIZATION FOR IMPROVING MARINE TRAJECTORY CLUSTERING AND ANOMALY DETECTION ». ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020 (25 août 2020) : 455–61. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2020-455-2020.
Texte intégralMichałowska, Katarzyna, Signe Riemer-Sørensen, Camilla Sterud et Ole Magnus Hjellset. « Anomaly Detection with Unknown Anomalies : Application to Maritime Machinery ». IFAC-PapersOnLine 54, no 16 (2021) : 105–11. http://dx.doi.org/10.1016/j.ifacol.2021.10.080.
Texte intégralPark, Jaemin, et Sungil Kim. « Maritime Anomaly Detection Based on VAE-CUSUM Monitoring System ». Journal of the Korean Institute of Industrial Engineers 46, no 4 (31 août 2020) : 432–42. http://dx.doi.org/10.7232/jkiie.2020.46.4.432.
Texte intégralLei, Po-Ruey. « A framework for anomaly detection in maritime trajectory behavior ». Knowledge and Information Systems 47, no 1 (19 mai 2015) : 189–214. http://dx.doi.org/10.1007/s10115-015-0845-4.
Texte intégralThèses sur le sujet "Maritime anomaly detection"
Riveiro, María José. « Visual analytics for maritime anomaly detection ». Doctoral thesis, Örebro universitet, Akademin för naturvetenskap och teknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-12783.
Texte intégralMaria Riveiro is also affiliated to Informatics Research Centre, Högskolan i Skövde
Information Fusion Research Program, Högskolan i Skövde
Abghari, Shahrooz, et Samira Kazemi. « Open Data for Anomaly Detection in Maritime Surveillance ». Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4807.
Texte intégralThis thesis investigated the potential open data as a complementary resource for Anomaly Detection (AD) in the Maritime Surveillance (MS) domain. A framework for AD was proposed based on the usage of open data sources along with other traditional sources of data. According to the proposed AD framework and the algorithms for implementing the expert rules, the Open Data Anomaly Detection System (ODADS) was developed. To evaluate the accuracy of the system, an experiment on the vessel traffic data was conducted and an accuracy of 99% was obtained for the system. There was a false negative case in the system results that decreased the accuracy. It was due to incorrect AIS data in a special situation that was not possible to be handled by the detection rules in the scope of this thesis. The validity of the results was investigated by the subject matter experts from the Swedish Coastguard. The validation results showed that the majority of the ODADS evaluated anomalies were true alarms. Moreover, a potential information gap in the closed data sources was observed during the validation process. Despite the high number of true alarms, the number of false alarms was also considerable that was mainly because of the inaccurate open data. This thesis provided insights into the open data as a complement to the common data sources in the MS domain and is concluded that using open data will improve the efficiency of the surveillance systems by increasing the accuracy and covering some unseen aspects of maritime activities.
Brax, Christoffer. « Anomaly detection in the surveillance domain ». Doctoral thesis, Örebro universitet, Akademin för naturvetenskap och teknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-16373.
Texte intégralChristoffer Brax forskar också vid högskolan i Skövde, Informatics Research Centre / Christoffer Brax also does research at the University of Skövde, Informatics Research Centre
Pellissier, Muriel. « Anomaly detection technique for sequential data ». Thesis, Grenoble, 2013. http://www.theses.fr/2013GRENM078/document.
Texte intégralNowadays, huge quantities of data can be easily accessible, but all these data are not useful if we do not know how to process them efficiently and how to extract easily relevant information from a large quantity of data. The anomaly detection techniques are used in many domains in order to help to process the data in an automated way. The anomaly detection techniques depend on the application domain, on the type of data, and on the type of anomaly.For this study we are interested only in sequential data. A sequence is an ordered list of items, also called events. Identifying irregularities in sequential data is essential for many application domains like DNA sequences, system calls, user commands, banking transactions etc.This thesis presents a new approach for identifying and analyzing irregularities in sequential data. This anomaly detection technique can detect anomalies in sequential data where the order of the items in the sequences is important. Moreover, our technique does not consider only the order of the events, but also the position of the events within the sequences. The sequences are spotted as anomalous if a sequence is quasi-identical to a usual behavior which means if the sequence is slightly different from a frequent (common) sequence. The differences between two sequences are based on the order of the events and their position in the sequence.In this thesis we applied this technique to the maritime surveillance, but this technique can be used by any other domains that use sequential data. For the maritime surveillance, some automated tools are needed in order to facilitate the targeting of suspicious containers that is performed by the customs. Indeed, nowadays 90% of the world trade is transported by containers and only 1-2% of the containers can be physically checked because of the high financial cost and the high human resources needed to control a container. As the number of containers travelling every day all around the world is really important, it is necessary to control the containers in order to avoid illegal activities like fraud, quota-related, illegal products, hidden activities, drug smuggling or arm smuggling. For the maritime domain, we can use this technique to identify suspicious containers by comparing the container trips from the data set with itineraries that are known to be normal (common). A container trip, also called itinerary, is an ordered list of actions that are done on containers at specific geographical positions. The different actions are: loading, transshipment, and discharging. For each action that is done on a container, we know the container ID and its geographical position (port ID).This technique is divided into two parts. The first part is to detect the common (most frequent) sequences of the data set. The second part is to identify those sequences that are slightly different from the common sequences using a distance-based method in order to classify a given sequence as normal or suspicious. The distance is calculated using a method that combines quantitative and qualitative differences between two sequences
Laxhammar, Rikard. « Conformal anomaly detection : Detecting abnormal trajectories in surveillance applications ». Doctoral thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-8762.
Texte intégralMcAbee, Ashley S. M. « Traffic pattern detection using the Hough transformation for anomaly detection to improve maritime domain awareness ». Thesis, Monterey, California : Naval Postgraduate School, 2013. http://hdl.handle.net/10945/38977.
Texte intégralTechniques for anomaly detection in the maritime domain by extracting traffic patterns from ship position data to generate atlases of expected ocean travel are developed in this thesis. An archive of historical data is used to develop a traffic density grid. The Hough transformation is used to extract linear patterns of elevated density from the traffic density grid, which can be considered the highways of the oceans. These highways collectively create an atlas that is used to define geographical regions of expected ship locations. Ship position reports are compared to the atlas of highways to flag as anomalous any ship that is not operating on an expected highway. The atlas generation techniques are demonstrated using automated information system (AIS) ship position data to detect highways in both open-ocean and coastal areas. Additionally, the atlas generation techniques are used to explore variability in ship traffic as a result of extreme weather and seasonal variation. Finally, anomaly detection is demonstrated by comparing AIS data from 2013 to the highways detected in the archive of data from 2012. The development of an automatic atlas generation technique that can be used to develop a definition of normal maritime behavior is the significant result of this thesis.
Osekowska, Ewa. « Design and Implementation of a Maritime Traffic Modeling and Anomaly Detection Method ». Licentiate thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-00600.
Texte intégralNguyen, Van Duong. « Variational deep learning for time series modelling and analysis : applications to dynamical system identification and maritime traffic anomaly detection ». Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2020. http://www.theses.fr/2020IMTA0227.
Texte intégralThis thesis work focuses on a class of unsupervised, probabilistic deep learning methods that use variational inference to create high capacity, scalable models for time series modelling and analysis. We present two classes of variational deep learning, then apply them to two specific problems related to the maritime domain. The first application is the identification of dynamical systems from noisy and partially observed data. We introduce a framework that merges classical data assimilation and modern deep learning to retrieve the differential equations that control the dynamics of the system. Using a state space formulation, the proposed framework embeds stochastic components to account for stochastic variabilities, model errors and reconstruction uncertainties. The second application is maritime traffic surveillance using AIS data. We propose a multitask probabilistic deep learning architecture can achieve state-of-the-art performance in different maritime traffic surveillance related tasks, such as trajectory reconstruction, vessel type identification and anomaly detection, while reducing significantly the amount data to be stored and the calculation time. For the most important task—anomaly detection, we introduce a geospatial detector that uses variational deep learning to builds a probabilistic representation of AIS trajectories, then detect anomalies by judging how likely this trajectory is
Erik, Bergenholtz. « False Alarm Reduction in Maritime Surveillance ». Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-12655.
Texte intégralHelldin, Tove. « Explanation Methods for Bayesian Networks ». Thesis, University of Skövde, School of Humanities and Informatics, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-3193.
Texte intégral
The international maritime industry is growing fast due to an increasing number of transportations over sea. In pace with this development, the maritime surveillance capacity must be expanded as well, in order to be able to handle the increasing numbers of hazardous cargo transports, attacks, piracy etc. In order to detect such events, anomaly detection methods and techniques can be used. Moreover, since surveillance systems process huge amounts of sensor data, anomaly detection techniques can be used to filter out or highlight interesting objects or situations to an operator. Making decisions upon large amounts of sensor data can be a challenging and demanding activity for the operator, not only due to the quantity of the data, but factors such as time pressure, high stress and uncertain information further aggravate the task. Bayesian networks can be used in order to detect anomalies in data and have, in contrast to many other opaque machine learning techniques, some important advantages. One of these advantages is the fact that it is possible for a user to understand and interpret the model, due to its graphical nature.
This thesis aims to investigate how the output from a Bayesian network can be explained to a user by first reviewing and presenting which methods exist and second, by making experiments. The experiments aim to investigate if two explanation methods can be used in order to give an explanation to the inferences made by a Bayesian network in order to support the operator’s situation awareness and decision making process when deployed in an anomaly detection problem in the maritime domain.
Chapitres de livres sur le sujet "Maritime anomaly detection"
Wu, Ying, Anthony Patterson, Rafael D. C. Santos et Nandamudi L. Vijaykumar. « Topology Preserving Mapping for Maritime Anomaly Detection ». Dans Computational Science and Its Applications – ICCSA 2014, 313–26. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09153-2_24.
Texte intégralJanssens, Jeroen, Eric Postma et Jaap van den Herik. « Density-Based Anomaly Detection in the Maritime Domain ». Dans Situation Awareness with Systems of Systems, 119–31. New York, NY : Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6230-9_8.
Texte intégralHoque, Ximi, et Sudhir Kumar Sharma. « Ensembled Deep Learning Approach for Maritime Anomaly Detection System ». Dans Proceedings of ICETIT 2019, 862–69. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30577-2_77.
Texte intégralThomopoulos, Stelios C. A., Constantinos Rizogannis, Konstantinos Georgios Thanos, Konstantinos Dimitros, Konstantinos Panou et Dimitris Zacharakis. « OCULUS Sea™ Forensics : An Anomaly Detection Toolbox for Maritime Surveillance ». Dans Business Information Systems Workshops, 485–95. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36691-9_41.
Texte intégralAnneken, Mathias, Yvonne Fischer et Jürgen Beyerer. « Quantitative Assessment of Anomaly Detection Algorithms in Annotated Datasets from the Maritime Domain ». Dans Studies in Computational Intelligence, 89–107. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33386-1_5.
Texte intégralKontopoulos, Ioannis, Iraklis Varlamis et Konstantinos Tserpes. « Uncovering Hidden Concepts from AIS Data : A Network Abstraction of Maritime Traffic for Anomaly Detection ». Dans Lecture Notes in Computer Science, 6–20. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38081-6_2.
Texte intégralNava, Jose, et Alejandro Osorio. « A Hybrid Intelligent Risk Identification Model for Configuration Management in Aerospace Systems ». Dans Handbook of Research on Military, Aeronautical, and Maritime Logistics and Operations, 319–45. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9779-9.ch017.
Texte intégralActes de conférences sur le sujet "Maritime anomaly detection"
Zor, C., et J. Kittler. « Maritime anomaly detection in ferry tracks ». Dans 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952636.
Texte intégralLane, R. O., D. A. Nevell, S. D. Hayward et T. W. Beaney. « Maritime anomaly detection and threat assessment ». Dans 2010 13th International Conference on Information Fusion (FUSION 2010). IEEE, 2010. http://dx.doi.org/10.1109/icif.2010.5711998.
Texte intégralRoy, Jean. « Anomaly detection in the maritime domain ». Dans SPIE Defense and Security Symposium, sous la direction de Craig S. Halvorson, Daniel Lehrfeld et Theodore T. Saito. SPIE, 2008. http://dx.doi.org/10.1117/12.776230.
Texte intégralAnneken, Mathias, Anne-Laure Jousselme, Sebastian Robert et Jurgen Beyerer. « Synthetic Trajectory Extraction for Maritime Anomaly Detection ». Dans 2018 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2018. http://dx.doi.org/10.1109/csci46756.2018.00204.
Texte intégralChatzikokolakis, Konstantinos, Dimitris Zissis, Marios Vodas, Giannis Spiliopoulos et Ioannis Kontopoulos. « A distributed lightning fast maritime anomaly detection service ». Dans OCEANS 2019 - Marseille. IEEE, 2019. http://dx.doi.org/10.1109/oceanse.2019.8867269.
Texte intégralRoy, Jean. « Rule-based expert system for maritime anomaly detection ». Dans SPIE Defense, Security, and Sensing, sous la direction de Edward M. Carapezza. SPIE, 2010. http://dx.doi.org/10.1117/12.849131.
Texte intégralMurray, Brian, et Lokukaluge P. Perera. « Unsupervised Trajectory Anomaly Detection for Situation Awareness in Maritime Navigation ». Dans ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/omae2020-18281.
Texte intégralAvram, Vladimir, Uwe Glasser et Hamed Yaghoubi Shahir. « Anomaly detection in spatiotemporal data in the maritime domain ». Dans 2012 IEEE International Conference on Intelligence and Security Informatics (ISI 2012). IEEE, 2012. http://dx.doi.org/10.1109/isi.2012.6284274.
Texte intégralVespe, M., I. Visentini, K. Bryan et P. Braca. « Unsupervised learning of maritime traffic patterns for anomaly detection ». Dans 9th IET Data Fusion & Target Tracking Conference (DF&TT 2012) : Algorithms & Applications. IET, 2012. http://dx.doi.org/10.1049/cp.2012.0414.
Texte intégralVandecasteele, Arnaud, et Aldo Napoli. « An enhanced spatial reasoning ontology for maritime anomaly detection ». Dans 2012 7th International Conference on System of Systems Engineering (SoSE). IEEE, 2012. http://dx.doi.org/10.1109/sysose.2012.6384120.
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