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Evaluation of Structural Changes of a Controlled Group Using Time-Sequential SNA
Woong Lee, Seong-Woong Yoon, Sang-Hoon Lee
A controlled group is closed compared to other organizations, which hinders collection of data and accurate analysis, so that it is hard to evaluate a controlled group’s power structure and predict future changes using usual analytical methods including sociological approach. Analyzing a controlled group using SNA can allow for evaluation of inner power structure by revealing the relationships between members and identifying members with central roles given limited data. In this study, in order to evaluate changes in power structure, time-sequential SNA research was conducted by analyzing eigenvector centrality, which reflects individual influence and reveals the overall power structure. The result showed an improvement in accuracy compared to other centralities that contain individual degree or closeness, and made it possible to presume structural changes such as promotion or purge of a member.
Automatic Recognition Algorithm of Unknown Ships on Radar
Hyun Chul Jung, Soung Woong Yoon, Sang Hoon Lee
Seeking and recognizing maritime targets are very important tasks for maritime safety. While searching for maritime targets using radar is possible, recognition is conducted without automatic identification system, radio communicator or visibility. If this recognition is not feasible, radar operator must tediously recognize maritime targets using movement features on radar base on know-how and experience. In this paper, to support the radar operator’s mission of continuous observation, we propose an algorithm for automatic recognition of an unknown ship using movement features on radar and a method of detecting potential ship related accidents. We extract features from contact range, course and speed of four types of vessels and evaluate the recognition accuracy using SVM and suggest a method of detecting potential ship related accidents through the algorithm. Experimentally, the resulting recognition accuracy is found to be more than 90% and presents the possibility of detecting potential ship related accidents through the algorithm using information of MV Sewol. This method is an effective way to support operator’s know-how and experience in various circumstances and assist in detecting potential ship related accidents.
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