• Journal of Navigation and Port Research
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• v.48 no.3
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• pp.164-170
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• 2024

The light buoy, a floating structure at sea, is prone to drifting due to external factors such as oceanic weather. This makes it imperative to monitor for any loss or displacement of buoys. In order to address this issue, the Ministry of Oceans and Fisheries aims to issue alerts for buoy displacement by analyzing historical buoy position data to detect patterns. However, periodic lifting inspections, which are conducted every two years, disrupt the buoy's location pattern. As a result, new patterns need to be analyzed after each inspection for location monitoring. In this study, buoy position data from various periods were analyzed using convex hull and distance-based clustering algorithms. In addition, the optimal data collection period was identified in order to accurately recognize buoy location patterns. The findings suggest that a nine-week data collection period established stable location patterns, explaining approximately 89.8% of the variance in location data. These results can improve the management of light buoys based on location patterns and aid in the effective monitoring and early detection of buoy displacement.

• Journal of Korea Multimedia Society
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• v.6 no.6
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• pp.1054-1061
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• 2003

Electronic ink is multimedia data that have emerged from the development of pen-based computers such as PDAs whose major input device is a stylus pen. Recently with the development and supply of pen-based mobile computers, the necessity of data processing techniques of electronic ink has increased. Techniques to develop a gesture-allowed text editor in electronic ink domain were studied in this paper. Gesture and electronic ink data are a promising feature of pen-based user interface, but they have not yet been fully exploited. A new gesture recognition algorithm to identify pen gestures and a segmentation method for electronic ink to execute gesture commands were proposed. An electronic ink editor, called GesEdit was developed using proposed algorithms. The gesture recognition algorithm is based on eight features of input strokes. Convex hull and input time have been used to segment electronic ink data into GC(Gesture Components) unit. A variety of experiments by ten people showed that the average recognition rate reached 99.6% for nine gestures.