Mining navigable water current information from ship-based big automated identification system data

Abstract The ship safety domain plays a significant role in collision risk assessment. However, few studies take the practical considerations of implementing this method in the vicinity of bridge-waters into account. Therefore, historical automatic identification system data is utilised to construct and analyse ship domains considering ship–ship and ship–bridge collisions. A method for determining the closest boundary is proposed, and the boundary of the ship domain is fitted by the least squares method. The ship domains near bridge-waters are constructed as ellipse models, the characteristics of which are discussed. Novel fuzzy quaternion ship domain models are established respectively for inland ships and bridge piers, which would assist in the construction of a risk quantification model and the calculation of a grid ship collision index. A case study is carried out on the multi-bridge waterway of the Yangtze River in Wuhan, China. The results show that the size of the ship domain is highly correlated with the ship's speed and length, and analysis of collision risk can reflect the real situation near bridge-waters, which is helpful to demonstrate the application of the ship domain in quantifying the collision risk and to characterise the collision risk distribution near bridge-waters.

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Marine Network Protocols and Security Risks

Journal of Cybersecurity and Privacy ◽

10.3390/jcp1020013 ◽

2021 ◽

Vol 1 (2) ◽

pp. 239-251

Author(s):

Ky Tran ◽

Sid Keene ◽

Erik Fretheim ◽

Michail Tsikerdekis

Keyword(s):

Network Protocols ◽

Network Protocol ◽

Identification System ◽

Automated Identification ◽

Security Risks ◽

Domain Specific ◽

Protocol Security ◽

Challenges And Opportunities

Marine network protocols are domain-specific network protocols that aim to incorporate particular features within the specialized marine context that devices are implemented in. Devices implemented in such vessels involve critical equipment; however, limited research exists for marine network protocol security. In this paper, we provide an analysis of several marine network protocols used in today’s vessels and provide a classification of attack risks. Several protocols involve known security limitations, such as Automated Identification System (AIS) and National Marine Electronic Association (NMEA) 0183, while newer protocols, such as OneNet provide more security hardiness. We further identify several challenges and opportunities for future implementations of such protocols.

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Listerial meningitis: Atypical strain and misidentification by the automated identification system (Vitek2)

Medical Journal Armed Forces India ◽

10.1016/j.mjafi.2018.07.003 ◽

2019 ◽

Vol 75 (1) ◽

pp. 99-101

Author(s):

Gurpreet Singh Bhalla ◽

Mahadevan Kumar ◽

Pooja Mahajan ◽

Kavita Sahai

Keyword(s):

Identification System ◽

Automated Identification

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Design and Implementation of an Efficient Rose Leaf Disease Detection using K-Nearest Neighbours

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c4213.099320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 21-27

Keyword(s):

Plant Disease ◽

Spot Size ◽

Extraction Process ◽

Disease Classification ◽

Identification System ◽

Automated Identification ◽

Accurate Identification ◽

Leaf Disease ◽

Key Factor ◽

Agriculture Products

Plants are prone to different diseases caused by multiple reasons like environmental conditions, light, bacteria, and fungus. These diseases always have some physical characteristics on the leaves, stems, and fruit, such as changes in natural appearance, spot, size, etc. Due to similar patterns, distinguishing and identifying category of plant disease is the most challenging task. Therefore, efficient and flawless mechanisms should be discovered earlier so that accurate identification and prevention can be performed to avoid several losses of the entire plant. Therefore, an automated identification system can be a key factor in preventing loss in the cultivation and maintaining high quality of agriculture products. This paper introduces modeling of rose plant leaf disease classification technique using feature extraction process and supervised learning mechanism. The outcome of the proposed study justifies the scope of the proposed system in terms of accuracy towards the classification of different kind of rose plant disease.

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Pseudo-outbreak of Enterococcus durans infections and colonization associated with introduction of an automated identification system software update.

Journal of Clinical Microbiology ◽

10.1128/jcm.34.11.2685-2687.1996 ◽

1996 ◽

Vol 34 (11) ◽

pp. 2685-2687 ◽

Cited By ~ 17

Author(s):

D A Singer ◽

E M Jochimsen ◽

P Gielerak ◽

W R Jarvis

Keyword(s):

Identification System ◽

System Software ◽

Automated Identification ◽

Software Update ◽

Enterococcus Durans

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Methods and Techniques for Automatic Identification System Data Reduction

10.1007/978-3-030-38836-2_12 ◽

2021 ◽

pp. 253-269

Author(s):

Claudia Ifrim ◽

Manolis Wallace ◽

Vassilis Poulopoulos ◽

Andriana Mourti

Keyword(s):

Data Reduction ◽

Automatic Identification ◽

Identification System ◽

Automatic Identification System ◽

Methods And Techniques ◽

System Data

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Real-Time Maritime Traffic Anomaly Detection Based on Sensors and History Data Embedding

Sensors ◽

10.3390/s19173782 ◽

2019 ◽

Vol 19 (17) ◽

pp. 3782 ◽

Cited By ~ 3

Author(s):

Julius Venskus ◽

Povilas Treigys ◽

Jolita Bernatavičienė ◽

Gintautas Tamulevičius ◽

Viktor Medvedev

Keyword(s):

Neural Network ◽

Data Processing ◽

Processing Time ◽

Heavy Traffic ◽

Sensor Data ◽

Identification System ◽

Automated Identification ◽

Maritime Traffic ◽

Abnormal Movements ◽

The Neural Network

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.

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