Matches in Nanopublications for { ?s ?p ?o <https://w3id.org/np/RAVXr2_uyvw9i6LNPO3DQHcDBw54LjcEd_l7-g6THj-9E/assertion>. }
- e35de860-bab9-40db-9cf3-4e7172929d1e type IPTC assertion.
- fd228f1f-0ec8-4ba1-af1d-fd973fdb0a82 type IPTC assertion.
- 2b2aa580-cb31-4fad-b70a-0ba329df9537 type NASA assertion.
- 2e780392-b00f-48a1-9399-8082a2f12a7c type NASA assertion.
- 2f6f73ef-de54-4311-bdee-a1e466dbc91e type NASA assertion.
- 9e845c12-e9ca-497d-afb9-2df07b304163 type NASA assertion.
- 12c53390-5200-45cf-a7e3-2c4b65a95eba type Phrase assertion.
- 1597ddad-a710-40e4-9e32-d40e40e124e2 type Phrase assertion.
- 20cf7606-ff2e-43b5-b87d-b37f563f0273 type Phrase assertion.
- 33acd928-1da5-40d7-8ff9-4e42f88a0e3c type Phrase assertion.
- 39945a58-4717-44b4-9f44-4bca319d445e type Phrase assertion.
- 4e5e569d-1904-4f91-873b-015381c4ad18 type Phrase assertion.
- 82015d22-5bb1-4352-b6eb-2f3165175aa0 type Phrase assertion.
- 8691aff0-a0a0-488e-a7ea-46b914e52ed4 type Phrase assertion.
- 899b07b2-b55f-40d4-bc93-8161b7668aad type Phrase assertion.
- a854a6ed-9c0f-45e5-aeaa-33dead69470b type Phrase assertion.
- ced5b15e-d817-408a-97fd-b8d9e18f511d type Phrase assertion.
- d3e6d06d-c72b-46d0-ae84-c1229b2cfc88 type Phrase assertion.
- f1184a07-b822-4eca-8794-48e7e7a68a8b type Phrase assertion.
- 35e5b93c-0517-4585-8537-c07df358b387 type Sentence assertion.
- 77e11ce5-f594-4bcd-be70-16d67a6e14a1 type Sentence assertion.
- 798f9c74-5063-4fea-a5d5-c91662c7f759 type Sentence assertion.
- 867ae333-96ba-4558-813d-a56de53e7a29 type Sentence assertion.
- 8b163d3d-918c-4a68-b2a0-4bcb57375de8 type Sentence assertion.
- 8c8fd292-f36f-4717-9957-574c76b29ef7 type Sentence assertion.
- 3949 type DefinedTerm assertion.
- c_a935cf3f type DefinedTerm assertion.
- 185bfb93-a586-4eef-bce4-6c5ac1ace208 type Place assertion.
- 22293247-7897-40c6-9db7-d14819803de0 type Place assertion.
- 4890851f-06c5-41ea-8b0f-7763f4735b18 type Place assertion.
- 5f2910cf-c1dc-411e-98b2-bcc96c9103e7 type Place assertion.
- a494aa34-fe8d-413a-a4dd-6f70d2c7c33b type Place assertion.
- c2ae66a8-52cb-4855-b53c-e14b855fbc00 type Place assertion.
- master type SWDocumentation assertion.
- vesseltype_identification_dae type SWDocumentation assertion.
- ro-crate-metadata.json type CreativeWork assertion.
- 7998d851-41e8-4c51-aa06-deff6fd5f09a type Dataset assertion.
- 0bdb922b-ce4e-4dda-af06-9182927662b9 type Dataset assertion.
- 4c580e28-b6c6-4c39-a0fa-95a016f28fc4 type Dataset assertion.
- 5bffb6c2-45e3-4a80-9242-5afd61c21063 type Dataset assertion.
- 636a64a4-ba87-4d16-8a67-455bfd74e7d6 type Dataset assertion.
- 706b8928-34f0-476a-93e4-b9bdcc97fa34 type Dataset assertion.
- bc6ab0f0-c443-4b11-a772-03fbcdb452c4 type Dataset assertion.
- f2c69dea-cb2a-4d42-92ee-fc041f5dc619 type Dataset assertion.
- 88fba8bd-f2f0-402e-8147-b73b71e8691a type MediaObject assertion.
- response-ais.html type MediaObject assertion.
- tle-fmt.php type MediaObject assertion.
- edit?usp=sharing type MediaObject assertion.
- view?usp=sharing type MediaObject assertion.
- view?usp=sharing type MediaObject assertion.
- tsar-project type MediaObject assertion.
- Two-line_element_set type MediaObject assertion.
- master type MediaObject assertion.
- marivisu-v2 type MediaObject assertion.
- pre-processing type MediaObject assertion.
- vesseltype_identification_dae type MediaObject assertion.
- 0AI6umItIl7BxUk9PVA type MediaObject assertion.
- apprentissage_auto_supervise_pour_detecter_les_deconnections_ais_volontaires.pdf type MediaObject assertion.
- 3e6f07ae-3da5-43ab-a7f9-4334ee01b8d2 type MediaObject assertion.
- c7d9b7cb-7192-471b-82f4-13fe89dc6906 type MediaObject assertion.
- ed59cb0a-e359-4f95-932d-88375b08daa7 type MediaObject assertion.
- d5d3a3ed-7bc1-40b9-b2cd-0496f599d0fe type MediaObject assertion.
- jmse10010112 type MediaObject assertion.
- 00vn06n10 type Organization assertion.
- 0AI6umItIl7BxUk9PVA type Thing assertion.
- d21eac9b-1655-4e66-a207-bc24e6b84423 type Organization assertion.
- view?usp=sharing type PresentationDigitalDocument assertion.
- 6e03b17e-e0d8-4581-b3b3-ad86c4a8fd6c type TimeReference assertion.
- d8c6b5cb-c11e-4629-8146-c31203b4b02a type TimeReference assertion.
- mailto:dusica@simula.no type Agent assertion.
- mailto:service-account-enrichment type Agent assertion.
- mailto:annefou@geo.uio.no type Agent assertion.
- mailto:pierbernabe@simula.no type Agent assertion.
- 0000-0001-6489-8858 type Agent assertion.
- 0000-0002-7715-7052 type Agent assertion.
- 00vn06n10 type Agent assertion.
- 0000-0002-1784-2920 type Agent assertion.
- 7998d851-41e8-4c51-aa06-deff6fd5f09a template "http://w3id.org/ro/earth-science#ExecutableResearchObjectTemplate" assertion.
- 42613c9d-9bea-4bc9-b475-1ac886a05d57 wasDerivedFrom 7998d851-41e8-4c51-aa06-deff6fd5f09a assertion.
- 3949 description "" assertion.
- c_a935cf3f description "" assertion.
- Two-line_element_set description "Description of Two-Line Element Set (TLE) from Wikipedia." assertion.
- vesseltype_identification_dae description "Private repository containing Anomaly Detection in Vessels Trajectories using Context-Aware Autoencoders" assertion.
- 636a64a4-ba87-4d16-8a67-455bfd74e7d6 description "Documentation and existing information about surveillance and detection of anomalies using Automatic Identification System data (ground and satellite)." assertion.
- d5d3a3ed-7bc1-40b9-b2cd-0496f599d0fe description "This Research Object contains AIS data (raw and pre-processed by Statsat AS, Norway). It is not public and has been provided by Statsat AS (Norway). If you are working at Simula, information on where to find pre-processed data on the EX3 is given in the Data RO (README.txt in the metadata folder). This dataset has been used for developing new machine learning algorithms for detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance in the framework of the T-SAR project (IKTPLUSS programme on reducing digital vulnerabilities, 10 MNOK from the Research Council of Norway, Norway) led by Simula Research Laboratory (Oslo, Norway)." assertion.
- jmse10010112 description "The automatic identification system (AIS) was introduced in the maritime domain to increase the safety of sea traffic. AIS messages are transmitted as broadcasts to nearby ships and contain, among others, information about the identification, position, speed, and course of the sending vessels. AIS can thus serve as a tool to avoid collisions and increase onboard situational awareness. In recent years, AIS has been utilized in more and more applications since it enables worldwide surveillance of virtually any larger vessel and has the potential to greatly support vessel traffic services and collision risk assessment. Anomalies in AIS tracks can indicate events that are relevant in terms of safety and also security. With a plethora of accessible AIS data nowadays, there is a growing need for the automatic detection of anomalous AIS data. In this paper, we survey 44 research articles on anomaly detection of maritime AIS tracks. We identify the tackled AIS anomaly types, assess their potential use cases, and closely examine the landscape of recent AIS anomaly research as well as their limitations." assertion.
- edit?usp=sharing description "Main document (Google doc) provided when willing to start with TSAR Overview." assertion.
- 88fba8bd-f2f0-402e-8147-b73b71e8691a description "Research Object with sample AIS data (in-situ)" assertion.
- view?usp=sharing description "Slides (private) presenting the T-SAR project." assertion.
- pre-processing description "n maritime traffic surveillance, detecting illegal activities, such as illegal fishing or transhipment of illicit products is a crucial task of the coastal administration. In the open sea, one has to rely on Automatic Identification System (AIS) messages transmitted by on-board transponders, which are captured by surveillance satellites. However, insincere vessels often intentionally shut down their AIS transponders to hide illegal activities. In the open sea, it is very challenging to differentiate intentional AIS shutdowns from missing reception due to protocol limitations, bad weather conditions or restricting satellite positions. This paper presents a novel approach for the detection of abnormal AIS missing reception based on self-supervised deep learning techniques and transformer models. Our method can process AIS messages in real-time, in particular, more than 500 Millions AIS messages per month, corresponding to the trajectories of more than 60 000 ships. The method is evaluated on 1-year of real-world data coming from four Norwegian surveillance satellites. The results show that the method can detect confirmed real-world intentional AIS shutdown operations." assertion.
- 5bffb6c2-45e3-4a80-9242-5afd61c21063 description "papers, conference proceeding generated by the TSAR project." assertion.
- c7d9b7cb-7192-471b-82f4-13fe89dc6906 description "The NorSat-3 microsatellite will be launched into space during spring 2021 with a radar detector developed at the Norwegian Defence Research Establishment (FFI). It will provide improved surveillance capability of the shipping traffic in Norwegian national waters. File downloaded from the Norwegian Defence Research Establishment (https://publications.ffi.no/nb/item/asset/dspace:7059/FFI-Facts_NorSat_Engelsk_web_v2.pdf)." assertion.
- bc6ab0f0-c443-4b11-a772-03fbcdb452c4 description "Bibliography collected on Automatic Identification System and detection of anomalies from AIS data (ground/satellite)." assertion.
- tsar-project description "Here we gather information about the project (notes taken during meetings, etc.). We use hackmd.io and text is written in markdown." assertion.
- 0AI6umItIl7BxUk9PVA description "Internally shared google drive with data and documents for T-SAR project" assertion.
- 3e6f07ae-3da5-43ab-a7f9-4334ee01b8d2 description "Distribution of samples on the surface of the globe." assertion.
- ed59cb0a-e359-4f95-932d-88375b08daa7 description "Major transportation surveillance protocols have not been specified with cyber securityin mind and therefore provide no encryption nor identification. These issues expose air and seatransport to false data injection attacks (FDIAs), in which an attacker modifies, blocks or emits fakesurveillance messages to dupe controllers and surveillance systems. There has been growing interestin conducting research on machine learning-based anomaly detection systems that address these newthreats. However, significant amounts of data are needed to achieve meaningful results with this typeof model. Raw, genuine data can be obtained from existing databases but need to be preprocessedbefore being fed to a model. Acquiring anomalous data is another challenge: such data is muchtoo scarce for both the Automatic Dependent Surveillance–Broadcast (ADS-B) and the AutomaticIdentification System (AIS). Crafting anomalous data by hand, which has been the sole methodapplied to date, is hardly suitable for broad detection model testing. This paper proposes an approachbuilt upon existing libraries and ideas that offers ML researchers the necessary tools to facilitatethe access and processing of genuine data as well as to automatically generate synthetic anomaloussurveillance data to constitute broad, elaborated test datasets. We demonstrate the usability of theapproach by discussing work in progress that includes the reproduction of related work, creation ofrelevant datasets and design of advanced anomaly" assertion.
- master description "Gitlab repository set up for reproducibility purposes." assertion.
- marivisu-v2 description "Marivisu serves as a demonstrator of the machine learning model developed to detect anomalies in the vessel trajectory. This work was supported by the Norwegian Research Council (RCN) TSAR project under contract 287893. Satellite AIS data used for model development and testing has been made available courteously by its owner, the Norwegian Coastal Administration (Kystverket)." assertion.
- view?usp=sharing description "In maritime traffic surveillance, detecting illegal activities, such as illegal fishing or transshipment of illicit products is a crucial task of the coastal administration. In the open sea, one has to rely on Automatic Identification System (AIS) message transmitted by on-board transponders, which are captured by surveillance satellites. However, insincere vessels often intentionally shut down their AIS transponders to hide illegal activities. In the open sea, it is very challenging to differentiate intentional AIS shutdowns from missing reception due to protocol limitations, bad weather conditions or restricting satellite positions. This paper presents a novel approach for the detection of abnormal AIS missing reception based on self-supervised deep learning techniques and transformer models. Using historical data, the trained model predicts if a message should be received in the upcoming minute or not. Afterwards, the model reports on detected anomalies by comparing the prediction with what actually happens. Our method can process AIS messages in real-time, in particular, more than 500 Millions AIS messages per month, corresponding to the trajectories of more than 60 000 ships. The method is evaluated on 1-year of real-world data coming from four Norwegian surveillance satellites. The results show that the method can detect confirmed real-world intentional AIS shutdown operations." assertion.