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Dealing with Imbalanced Data for GPS Trajectory Outlier Detection

Van-Chau Nguyen, Anh Le Van Quoc, Van Chien Nguyen

Annals of Computer Science and Information Systems

2022

Vol. 33

69--74

Detecting abnormal GPS trajectories derived by the mobility of people, cars, buses, and taxis plays a crucial role in developing applications for intelligent transportation systems. Outlier detection based on classification models is among promising approaches but it faces the imbalanced data problem, where instances labeled as abnormal have a very low number of observations. In this paper, we propose a framework that employs methods to deal with imbalanced data to the problem of GPS trajectory outlier detection. Our experiments show that dealing with imbalanced data beforehand can improve the performance of outlier detection models.

Optimizing the operation of a photovoltaic generator by a genetically tuned fuzzy controller

Drir N, Barazane L., Loudini M.

Archives of Control Sciences

2013

Vol. 23, no. 2

145--167

This paper presents design and application of advanced control scheme which integrates fuzzy logic concepts and genetic algorithms to track the maximum power point in photovoltaic system. The parameters of adopted fuzzy logic controller are optimized using genetic algorithm with innovative tuning procedures. The synthesized genetic algorithm which optimizes fuzzy logic controller is implemented and tested to achieve a precise control of the maximum power point response of the photovoltaic generator. The performance of the adopted control strategy is examined through a series of simulation experiments which prove good tracking properties and fast response to changes of different meteorological conditions such as isolation or temperature.