Abstract
The automated identification system of vessel movements receives a huge amount of multivariate, heterogeneous sensor data, which should be analyzed to make a proper and timely decision on vessel movements. The large number of vessels makes it difficult and time-consuming to detect abnormalities, thus rapid response algorithms should be developed for a decision support system to identify abnormal movements of vessels in areas of heavy traffic. This paper extends the previous study on a self-organizing map application for processing of sensor stream data received by the maritime automated identification system. The more data about the vessel’s movement is registered and submitted to the algorithm, the higher the accuracy of the algorithm should be. However, the task cannot be guaranteed without using an effective retraining strategy with respect to precision and data processing time. In addition, retraining ensures the integration of the latest vessel movement data, which reflects the actual conditions and context. With a view to maintaining the quality of the results of the algorithm, data batching strategies for the neural network retraining to detect anomalies in streaming maritime traffic data were investigated. The effectiveness of strategies in terms of modeling precision and the data processing time were estimated on real sensor data. The obtained results show that the neural network retraining time can be shortened by half while the sensitivity and precision only change slightly.
Highlights
The maritime industry is an important part of the global trade system with a growing volume, intensity, and needs
Automated data gathering systems (e.g., Automatic Identification System) return larger and larger trajectory datasets, which are challenging for human-based analysis and anomaly detection [4]
This paper extends the previous study on a self-organizing map application, which is trained in an unsupervised way using competitive learning, for processing of sensors stream data in order to detect abnormal vessel movement in maritime traffic
Summary
The maritime industry is an important part of the global trade system with a growing volume, intensity, and needs. The maritime anomaly or abnormal movement detection is one of the control techniques. It is based on vessel trajectory analysis and search of irregular, illegal, and other anomalous appearances in trajectory data [3]. Movement anomalies are detected as history-based deviations of vessel’s trajectory data, which can be problematic considering massive trajectory data streams. In this case, constant estimation of historical and context data means permanent need for system retraining.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
