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Development of automatic collision avoidance system and quantitative evaluation of the manoeuvring results

Nakamura S., Okada N.

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

2019

Vol. 13, no. 1

133--141

The automatic collision avoidance system introduced in this paper is a system constantly calculating optimal manoeuvring method from the risk and economic preference in the ship manoeuvring space where the course change and the deceleration are performed. The authors also propose a system that quantitatively evaluates the collision avoidance manoeuvring results. Based on the evaluation results of this system, the authors are setting parameters so that ship manoeuvring that does not give anxiety to target ships to be avoided is also realized in automatic collision avoidance manoeuvring. In addition, comparison between the manoeuvring results of the automatic collision avoidance system and the veteran captain's manoeuvring results was quantitatively compared by the proposed evaluation system. Verification experiments were successfully conducted to verify the effectiveness of the proposed automatic collision avoidance system on the actual ship navigating in congested waters.

Classification of tactile event-related potential elicited by Braille display for brain-computer interface

Hori J., Okada N.

Biocybernetics and Biomedical Engineering

2017

Vol. 37, no. 1

135--142

To construct brain–computer interface (BCI), an event-related potential (ERP) induced by a tactile stimulus is investigated in this paper. For ERP-based BCI, visual or auditory information is frequently used as the stimulus. In the present study, we focus on tactile sensations to reserve their visual and auditory senses for other activities. Several patterns of mechanical tactile stimulation were applied to the index fingers of both hands using two piezo actuators that were used as a braille display. Human experiments based on the oddball paradigm were carried out. Subjects were instructed to pay attention to unusual target stimuli while avoiding other frequent non-target stimuli. The extracted features were classified by applying stepwise linear discriminant analysis. As a result, an accuracy of 85% and 60% were obtained for 2- and 4-class classification, respectively. The accuracy was improved by increasing the number of electrodes even when short stimulus intervals were used.