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A Stay Detection Algorithm Using GPS Trajectory and Points of Interest Data
고은종,류창훈,최고야,정계동,권순철,황치곤 한국인터넷방송통신학회 2023 International Journal of Internet, Broadcasting an Vol.15 No.3
Points of interest (POIs) are widely used in tourism recommendations and to provide information about areas of interest. Currently, situation judgement using POI and GPS data is mainly rule-based. However, this approach has the limitation that inferences can only be made using predefined POI information. In this study, we propose an algorithm that uses POI data, GPS data, and schedule information to calculate the current speed, location, schedule matching, movement trajectory, and POI coverage, and uses machine learning to determine whether to stay or go. Based on the input data, the clustered information is labelled by k-means algorithm as unsupervised learning. This result is trained as the input vector of the SVM model to calculate the probability of moving and staying. Therefore, in this study, we implemented an algorithm that can adjust the schedule using the travel schedule, POI data, and GPS information. The results show that the algorithm does not rely on predefined information, but can make judgements using GPS data and POI data in real time, which is more flexible and reliable than traditional rule-based approaches. Therefore, this study can optimize tourism scheduling. Therefore, the stay detection algorithm using GPS movement trajectories and POIs developed in this study provides important information for tourism schedule planning and is expected to provide much value for tourism services.
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김욱진,김동섭,김상훈,김진백,고은종,강시용 한국작물학회 2010 Journal of crop science and biotechnology Vol.13 No.4
Seventeen kenaf varieties collected from several regions around Asia and Europe were grown in Korea and their genetic diversity was analyzed using morphological characters and AFLP technique. In the morphological analysis, the 17 varieties were divided into two major groups according to stem diameter, plant height, and flowering periods. The late varieties, which could yield more biomass compared with the early-medium varieties, were included in one of two major groups . Nonetheless, it is difficult to identify individual varieties based on morphological characters because of their limited variation. For the AFLP analysis, 34 primer combinations generated a total of 3,193 polymorphic bands (out of 3,914) with a polymorphic rate of 82%. The clusters were divided into two major groups with a similarity coefficient of 0.63 by UPGMA analysis method; but each group did not show a common tendency. Additionally, the results of the AFLP analysis did not show similar tendency compared with morphological data, a result that might be explained in terms of convergent evolution, i.e. the acquisition of morphologically similar traits between distinctly unrelated varieties.
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