Ship-ship collisions are rare events mainly caused by human errors. In an ever growing global trade world, the avoidance of such accidents is of great importance as they may cause significant human, environmental, and financial consequences. A collision warning system providing to Maritime related authorities (Vessel Traffic Services, Coast Guard authorities, Port authorities, other) real-time alerts for ship-ship encounter may significantly contribute to the reduction of collision accidents and the increase of navigation safety. In this paper a Fuzzy inference system (FIS) is proposed to estimate risk collision for pairs of ships receiving as input parameters calculated based on data obtained from the Automatic Identification System (AIS) which is installed onboard ships. The proposed system operates in real time examining pairs of ships that recently transmitted an AIS signal while discards, at an early processing stage, those pairs which do not satisfy specific conditions in order to reduce computational burden. Experiments have been carried out with real data and the results show that the proposed approach is effective in automatic alert generation for ships involved in near ship-ship situations.
Maritime “awareness” is currently a top priority for Europe in regards with the marine environment and climate change, as well as the maritime security, border control against irregular immigration and safety. MARINE-EO is the first European Earth Observation (EO) Pre-Commercial Procurement (PCP) project and aims at the following objectives: (i) Develop, test and validate two sets of demand-driven EO-based services, adopted on open standards, bringing incremental or radical innovations in the field of maritime awareness and leveraging on the existing Copernicus Services and other products from the Copernicus portfolio, (ii) Propose a set of “support” / “envelop” services which will better integrate the EO-based services to the operational logic and code of conduct, (iii) Strengthen transnational collaboration in maritime awareness sector by facilitating knowledge transfer and optimization of resources for the public authorities participating in the buyers group.
In this paper the architecture of an autonomous human behavior detection system is presented. The proposed system architecture is intended for Security and Safety surveillance systems that aim to identify adverse events or behaviors which endanger the safety of people or their well-being. Applications include monitoring systems for crowded places (Malls, Mass transport systems, other), critical infrastructures, or border crossing points. The proposed architecture consists of three modules: (a) the event detection module combined with a data fusion component responsible for the fusion of the sensor inputs along with relevant high level metadata, which are pre-defined features that are correlated with a suspicious event, (b) an adaptive learning module which takes inputs from official personnel or healthcare personnel about the correctness of the detected events, and uses it in order to properly parameterise the event detection algorithm, and (c) a statistical and stochastic analysis component which is responsible for specifying the appropriate features to be used by the event detection module. Statistical analysis estimates the correlations between the features employed in the study, while stochastic analysis is used for the estimation of dependencies between the features and the achieved system performance.
KEYWORDS: Reliability, Analytics, Detection and tracking algorithms, Telecommunications, Data modeling, Human-machine interfaces, Flame detectors, Visualization, Information fusion, Sensors, Web 2.0 technologies, Information operations
In this paper a solution is presented aiming to assist the early detection and localization of a fire incident by exploiting
crowdsourcing and unofficial civilian online reports. It consists of two components: (a) the potential fire incident
detection and (b) the visualization component. The first component comprises two modules that run in parallel and aim
to collect reports posted on public platforms and conclude to potential fire incident locations. It collects the public
reports, distinguishes reports that refer to a potential fire incident and store the corresponding information in a structured
way. The second module aggregates all these stored reports and conclude to a probable fire location, based on the
amount of reports per area, the time and location of these reports. In further the result is entered to a fusion module
which combines it with information collected by sensors if available in order to provide a more accurate fire event
detection capability. The visualization component is a fully – operational public information channel which provides
accurate and up-to-date information about active and past fires, raises awareness about forest fires and the relevant
hazards among citizens. The channel has visualization capabilities for presenting in an efficient way information
regarding detected fire incidents fire expansion areas, and relevant information such as detecting sensors and reporting
origin. The paper concludes with insight to current CONOPS end user with regards to the inclusion of the proposed
solution to the current CONOPS of fire detection.
KEYWORDS: Humidity, Monte Carlo methods, Infrared sensors, Temperature metrology, Sensors, Flame detectors, Data fusion, Web 2.0 technologies, Control systems, Probability theory, Reliability, Unmanned aerial vehicles, Infrared cameras, Environmental sensing
The aim of this paper is to present the sensor monitoring and decision level fusion scheme for early fire detection which
has been developed in the context of the AF3 Advanced Forest Fire Fighting European FP7 research project, adopted
specifically in the OCULUS-Fire control and command system and tested during a firefighting field test in Greece with
prescribed real fire, generating early-warning detection alerts and notifications. For this purpose and in order to improve
the reliability of the fire detection system, a two-level fusion scheme is developed exploiting a variety of observation
solutions from air e.g. UAV infrared cameras, ground e.g. meteorological and atmospheric sensors and ancillary sources
e.g. public information channels, citizens smartphone applications and social media. In the first level, a change point
detection technique is applied to detect changes in the mean value of each measured parameter by the ground sensors
such as temperature, humidity and CO2 and then the Rate-of-Rise of each changed parameter is calculated. In the second
level the fire event Basic Probability Assignment (BPA) function is determined for each ground sensor using Fuzzy-logic
theory and then the corresponding mass values are combined in a decision level fusion process using Evidential
Reasoning theory to estimate the final fire event probability.
Oculus Sea is a complete solution regarding maritime surveillance and communications at Local as well as Central Command and Control level. It includes a robust and independent track fusion service whose main functions include: 1) Interaction with the User to suggest the fusion of two or more tracks, confirm Track ID and Vessel Metadata creation for the fused track, and suggest de-association of two tracks 2) Fusion of same vessel tracks arriving simultaneously from multiple radar sensors featuring track Association, track Fusion of associated tracks to produce a more accurate track, and Multiple tracking filters and fusion algorithms 3) Unique Track ID Generator for each fused track 4) Track Dissemination Service. Oculus Sea Track Fusion Service adopts a system architecture where each sensor is associated with a Kalman estimator/tracker that obtains an estimate of the state vector and its respective error covariance matrix.
Finally, at the fusion center, association and track state estimation fusion are carried out. The expected benefits of this
system include multi-sensor information fusion, enhanced spatial resolution, and improved target detection.
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