Identification of testing scenario from AIS data for intelligent ship navigation

2021 ◽  
Author(s):  
Feixiang Zhu ◽  
Zhihong Ma
Keyword(s):  
2021 ◽  
Vol 1865 (4) ◽  
pp. 042110
Author(s):  
Tao Lin ◽  
Huihui Wang ◽  
Mengyin Ma ◽  
Haotian Zhang

Author(s):  
Andreas Brandsæter ◽  
Ottar L Osen

The advent of artificial intelligence and deep learning has provided sophisticated functionality for sensor fusion and object detection and classification which have accelerated the development of highly automated and autonomous ships as well as decision support systems for maritime navigation. It is, however, challenging to assess how the implementation of these systems affects the safety of ship operation. We propose to utilize marine training simulators to conduct controlled, repeated experiments allowing us to compare and assess how functionality for autonomous navigation and decision support affects navigation performance and safety. However, although marine training simulators are realistic to human navigators, it cannot be assumed that the simulators are sufficiently realistic for testing the object detection and classification functionality, and hence this functionality cannot be directly implemented in the simulators. We propose to overcome this challenge by utilizing Cycle-Consistent Adversarial Networks (Cycle-GANs) to transform the simulator data before object detection and classification is performed. Once object detection and classification are completed, the result is transferred back to the simulator environment. Based on this result, decision support functionality with realistic accuracy and robustness can be presented and autonomous ships can make decisions and navigate in the simulator environment.


2011 ◽  
Vol 38 (17-18) ◽  
pp. 2290-2305 ◽  
Author(s):  
Yanzhuo Xue ◽  
D. Clelland ◽  
B.S. Lee ◽  
Duanfeng Han

2012 ◽  
Vol 22 (2) ◽  
pp. 95-103
Author(s):  
Ante Bukša ◽  
Ivica Šegulja ◽  
Vinko Tomas

By adjusting the maintenance approach towards the significant components of ship’s engines and equipment, through the use of operational data from the ship machinery’s daily reports, higher operability and navigation safety can be achieved. The proposed maintenance adjustment model consists of an operation data analysis and risk analysis. The risk analysis comprises the definition of the upper and the lower risk criterion, as well as the definition of a risk index. If the risk index is higher than the lower risk criterion, the component is significant, while it is not significant and has an acceptable risk index if the risk index is lower than the lower risk criterion. For each significant component with a risk index found to be “unacceptable” or “undesirable”, an efficient maintenance policy needs to be adopted. The assessment of the proposed model is based on data regarding the power engine original operation throughout a 13-year period. The results of engine failure examinations reveal that the exhaust valve is the most vulnerable component with the highest rate of failure. For this reason the proposed model of adjusting the maintenance approach has been tested on the exhaust valve sample. It is suggested that the efforts to achieve higher ship operability and navigation safety should go in the direction of periodical adjustments of the maintenance approach i.e. choosing an efficient maintenance policy by reducing the risk indices of the significant engine components. KEY WORDS: maintenance adjustment approach, risk analysis, risk index, lower risk criterion, upper risk criterion, significant components, ship navigation


2011 ◽  
Vol 1 (1) ◽  
pp. 52-59
Author(s):  
Jin-Ho Bae ◽  
Chong-Hyun Lee ◽  
Chang-Ku Hwang
Keyword(s):  

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