• Journal of Navigation and Port Research
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• v.37 no.4
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• pp.401-407
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• 2013

In parallel with the development of ship equipment, bridge systems have been improved, but marine accidents due to human error have not been reduced. Recently, research in nautical bridge equipment has focused on suitable ergonomic designs in order to reduce these errors due to human factors. In a bridge of a ship, there are numerous auditory signals that deliver important information clearly to the sailors. However, only a few studies have been conducted related to the human recognition of these auditory signals. There are three types of auditory signals: voice alarms, abstract sounds, and auditory icons. This study was conducted in order to design more appropriate auditory icons using a sensibility evaluation method. The auditory icons were rated to have five warning situations (engine failure, fire, steering failure, low power, and collision) using the Semantic Differential Method. It is expected that the results of this study will be used as basic data for auditory displays inside bridges and for integrated bridge alarm systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.2
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• pp.113-118
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• 2017

Various studies on the method of ship collision risk assessment for alarm have been reported constantly, and the result of the studies is applied to navigation devices. However, it is known that navigators ignore or turn off frequent alarms from the devices of predicting collision risk, because they may avoid collisions in the most of situations. In oder to make the prediction of ship collision risk more useful, it is necessary to consider the customary actions of ship collision avoidance. This paper proposes a system of analyzing collision avoidance measures of ships according to the types of encounter and managing the avoidance history of each ship. The core module of the system is designed as a neuro-fuzzy based inference system, and the test of the module validates the proposed system.