Tunis University

About: Tunis University is a education organization based out in Tunis, Tunisia. It is known for research contribution in the topics: Population & Thin film. The organization has 11745 authors who have published 15400 publications receiving 154900 citations. The organization is also known as: University of Tunis & UT.

Unsupervised learning of finite full covariance multivariate generalized Gaussian mixture models for human activity recognition

Abstract: We propose in this paper to recognize human activities through an unsupervised learning of finite multivariate generalized Gaussian mixture model. We address an important cue in finite mixture model which is the estimation of the mixture model’s parameters for a full covariance matrix. We have developed a novel learning algorithm based on Fixed-point covariance matrix estimator combined with the Expectation-Maximization algorithm. Furthermore, we have proposed an appropriate minimum message length (MML) criterion to deal with model selection problem. We evaluated our proposed method on synthetic datasets and a challenging application namely : Human activity recognition from images and videos. The obtained resutls show clearly the merits of our proposed framework which has better capabilities with full covariance matrix when modeling correlated data.

Stalling-Free Control Strategies for Oscillating-Water-Column-Based Wave Power Generation Plants

Abstract: The rising use of renewable power generation plants is highlighting the need for an integrated research combining multiple disciplines in order to achieve a commercially competitive technology stage. The demonstrative NEREIDA wave power plant installed in the northern coast of Spain constitutes a good example of this effort. This paper deals with the design, modeling, and control of the oscillating-water-column-based wave energy converter in order to maximize the power output of the NEREIDA power plant. The power optimization relies on two control strategies proposed to avoid the stalling behavior, a characteristic drawback of the Wells turbine, which limits the system's power. The first control strategy is an airflow control using a PID controller tuned by the particle swarm optimization algorithm and its recent memetic variant called fractional particle swarm optimization memetic algorithm. This controller will control a throttle valve to regulate the airflow in the turbine duct. The second one consist of adequately controlling the rotational speed of the generator by means of the rotor-side converter of the back-to-back converter connected to the doubly fed induction generator to provide a swift way to respond to the rapid variations in the turbine speed. The results show that both controls provide a higher power generation compared to the uncontrolled case.

New transform iterative method for solving some Klein-Gordon equations

Abstract: In this study, we treat some Klein-Gordon equations (KGEs). We propose a novel iterative approach called the Elzaki iterative method (EIM). This method, which clearly depends on the choice of the initial values, is based on the new iteration method (NIM) and the Elzaki transformation. We show that the EIM could be more valid and reliable approach than the NIM. We propose an analytical approximation of a solution for KGEs for which only a few iterations are necessary to obtain a semi-analytical solution without a loss of precision.