• Journal of Advanced Navigation Technology
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• v.18 no.2
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• pp.128-133
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• 2014

This paper is the study of BNWAS based on TLC. The functionality of BNWAS and its operations are investigated through its international standard. But the BNWAS to be used currently in the ship have difficulty in monitoring. Several kinds of data are generated from many equipments in BNWAS, such as NMEA-0183 data or NMEA-2000. Although these data are mainly used for the safe navigation of ship, their usability may be enhanced if they are managed to control the BNWAS equipment with sensors. The purpose of this system is prevent the marine accidents on sailing voyages due to drowsiness of watchers. On Night sailing, watcher is collected the navigation information from multiple devices and he determines the safe operation of the ship through continuous monitoring.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.8C
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• pp.711-720
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• 2012

In this paper, we introduce the implementation of driver fatigue monitering system and its result. Input video device is selected commercially available web-cam camera. Haar transform is used to face detection and adopted illumination normalization is used for arbitrary illumination conditions. Facial image through illumination normalization is extracted using Haar face features easily. Eye candidate area through illumination normalization can be reduced by anthropometric measurement and eye detection is performed by PCA and Circle Mask mixture model. This methods achieve robust eye detection on arbitrary illumination changing conditions. Drowsiness state is determined by the level on illumination normalize eye images by a simple calculation. Our system alarms and operates seatbelt on vibration through controller area network(CAN) when the driver's doze level is detected. Our algorithm is implemented with low computation complexity and high recognition rate. We achieve 97% of correct detection rate through in-car environment experiments.