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Analyzing the factors affecting the safe maritime navigation for training apprentice officers

Lee M. K., Park S. W., Park Y. S., Park M. J., Lee E. K.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2019

Vol. 13, no. 2

393--399

One of the primary factors that affect the safe maritime navigation is the insufficient experience and skill of an apprentice officer, which may be improved using simulation-based training by ensuring operational efficiency. This study aims to determine appropriate factors for achieving effective and intensive simulation-based training of apprentice officers and present the guidelines for such a training scheme. Initially, a marine traffic risk model, which interprets and accurately measures the risk of collision with other vessels, is analyzed to derive the most influential factors in safe navigation. Subsequently, simulation experiments are conducted by applying machine learning to verify the required safe navigation factors for effectively training the apprentice officers. As a result of the above analysis, it was confirmed that the factor affecting safe maritime navigation was the distance from other vessels. Finally, the differences between these distances in the simulations are analyzed for both the apprentice officers and the experienced officers, and the guidelines corresponding to both these cases are presented. This study has the limitation because of the difference between the ship maneuver simulation and the actual ship navigation. This can be resolved based on the results of this study, in combination with the actual navigation data.

Bayesian model for multimodal sensory information fusion in humanoid

Wong W. K., Loo L. C., Neoh T. M., Liew Y. W., Lee E. K.

Journal of Automation Mobile Robotics and Intelligent Systems

2011

Vol. 5, No. 1

16-22

In this paper, the Bayesian model for bimodal sensory information fusion is presented. It is a simple and biological plausible model used to model the sensory fusion in human’s brain. It is adopted into humanoid robot to fuse the spatial information gained from analyzing auditory and visual input, aiming to increase the accuracy of object localization. Bayesian fusion model requires prior knowledge on weights for sensory systems. These weights can be determined based on standard deviation (SD) of unimodal localization error obtained in experiments. The performance of auditory and visual localization was tested under two conditions: fixation and saccade. The experiment result shows that Bayesian model did improve the accuracy of object localization. However, the fused position of the object is not accurate when both of the sensory systems were bias towards the same direction.