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Issue title: Special Section: Intelligent Data Aggregation Inspired Paradigm and Approaches in IoT Applications
Guest editors: Xiaohui Yuan and Mohamed Elhoseny
Article type: Research Article
Authors: Liu, Lizhenga; b; * | Liu, Fangaia | Ky, Byronc; *
Affiliations: [a] Shandong Normal University, Jinan, Shandong, China | [b] Shandong University of Finance and Economics, Jinan, Shandong, China | [c] Tarrant County College, Texas, USA
Correspondence: [*] Corresponding author. Lizheng Liu, Shandong Normal University, Jinan, Shandong, 250014, China. [email protected] Byron Ky, Tarrant County College, Texas, USA. E-mail: [email protected].
Abstract: Due to the increased development and applications of satellite communication, GPS equipment, video tracking and other communication technologies, the trajectory prediction of various SIs can be accurately predicted and tracked. Based on these technologies, mobile targets are extensively used in many applications. A large amount of trajectory data predicted by trajectory can be collected from the positioning terminal of the moving target by the signal-receiving device. This paper analyzes the application of computer technology in trajectory prediction of moving objects in data mining algorithm and proposes a trajectory analysis method based on structure and cloud computing motion capture algorithm. The motion and trajectory characteristics of moving target trajectories are analyzed from microcosmic angle. By comparing the structural features of the extracted trajectory and the trajectory predicted by the trajectory of the moving object, the motion characteristics of the object can be analyzed more comprehensively. In addition, the sensitivity of the trajectory structure can be flexibly adjusted by setting the weight of the trajectory structure, so that the trajectory of the moving target can be analyzed and predicted in an effective and flexible way.
Keywords: Data mining algorithm, moving target trajectory, prediction, computer technology, application
DOI: 10.3233/JIFS-179093
Journal: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 371-379, 2019
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