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Developing generative adversarial nets to extend training sets and optimize diiscrete actions

Zhang R. L., Furusho M.

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

2019

Vol. 13. no. 4

875--880

This study proposes the use of generative adversarial networks (GANs) to solve two crucial problems in the unmanned ship navigation: insufficient training data for neural networks and convergence of optimal actions under discrete conditions. To achieve smart collision avoidance of unmanned ships in various sea environments, first, this study proposes a collision avoidance decision model based on a deep reinforcement learning method. Then, it utilizes GANs to generate enough realistic image training sets to train the decision model. According to generative network learning, the conditional probability distribution of ship maneuvers is learnt (action units). Subsequently, the decision system can select a reasonable action to avoid the obstacles due to the discrete responses of the generated model to different actions and achieve the effect of intelligent collision avoidance. The experimental results showed that the generated target ship image set can be used as the training set of decision neural networks. Further, a theoretical reference to optimize the optimal convergence of discrete actions is provided.

Active learning in maritime education

Kunieda Y., Kashima Y., Kido H., Murai K.

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

2017

Vol. 11, no. 1

135--140

In the field of education, the importance of active learning as a more effective educational method has recently been noted. The element of active learning was introduced to the anchoring training of the training ship for third-year students of Tokyo University of Marine Science and Technology, and its effects have been verified. The learning effect of training was confirmed by the questionnaire results of the students. Moreover, the self-evaluations of student and evaluation of instructor confirmed the skill of grasping a ship's position with improved accuracy and learning effects. Self-evaluation and evaluation by the other student, which were introduced as the element of active learning, both demonstrated a positive learning effect. Furthermore, since an effective training method was examined, a result is reported.