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Swarm Reconnaissance Drone System for Efficient Object Detection
SungTae Moon, Jihoon Jeon, Yongwoo Kim
http://doi.org/10.5626/JOK.2022.49.9.715
With the recent development in drone technology, drones are being used in numerous industries such as cultural performances, logistics delivery, and traffic monitoring. In particular, as drones are used in reconnaissance fields such as the search for missing people and intruder detection, efficient mission performance has become possible. For effective reconnaissance, it is necessary to quickly monitor a large area and find a target in real-time. However, the current system cannot obtain real-time reconnaissance results because it is difficult to process inside the drone due to its performance limitations. In addition, it is difficult to conduct integrated commands and share information because it is judged based on the images obtained individually from the drone. This paper proposes a pruning algorithm and active swarm reconnaissance system for object detection based on stitched drone images. Using four drones, the proposed system verifies the real-time object detection and swarm operation system.
Extraction of Cognitive Psychological Features of Mobile Gamers and Improvement of Purchases Prediction Performance
Jihoon Jeon, Seongil Yang, KyungJoong Kim
http://doi.org/10.5626/JOK.2019.46.9.892
In-game purchases are one of the important factors that directly affect a company"s revenue. In total, 95% of gamers do not pay for in-game purchases, meaning that a small number of gamers are responsible for most of the revenue of the company behind their games. For this reason, game companies must maintain and augment these few purchasing gamers. In this paper, we extracted seven cognitive psychological features (competitive, challenge, loyal, social, activity, efficient, and sincerity) that can be used to estimate the cognitive psychology of a gamer by using log data of a mobile RPG game. We analyzed the gamers, classified by payment amount, based on seven cognitive psychological features. As a result, the cognitive psychological features and payment amount of the gamers could be correlated. In addition, using seven cognitive psychological features, we predicted the purchasing behavior of gamers with high accuracy. This implies that gamers can be analyzed based on their cognitive psychology and the gamer"s purchases can be predicted with comparatively high performance.
Mobile Gamer Categorization with Archetypal Analysis and Cognitive-Psychological Features from Log Data
Jihoon Jeon, Dumim Yoon, Seongil Yang, Kyungjoong Kim
http://doi.org/10.5626/JOK.2018.45.3.234
The study of classifying gamer types or analyzing the characteristics of gamers is a field of interest for data analysis researchers. From the past to the present, much research has been done on gamer categorization and gamer analysis. However, most studies use surveys or bio-signals, which is not practical because it is difficult to obtain large amounts of data. Even if the game log is used, it is difficult to analyze the psychology of the gamer because the gamer is categorized and analyzed by extracting only statistical values. However, if we can extract the cognitive psychology information of the gamer from the basic game log, we can analyze the gamer more intuitively and easily. In this paper, we extracted eight cognitive psychological features representing the behavior and psychological information of the gamer using Crazy Dragon"s game log, which is a mobile Role-Playing-Game (RPG). In addition, we classified gamers based upon cognitive psychological features and analyzed them using eight cognitive psychological features. As a result, most gamers were highly correlated with one or two types.
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