This paper presents a new observer design methodology for a time varying actuator fault estimation. A new linear matrix inequality (LMI) design algorithm is developed to tackle the limitations (e.g. equality constraint and robustness problems) of the well known so called fast adaptive fault estimation observer (FAFE). The FAFE is capable of estimating a wide range of time-varying actuator fault...

In this paper we develop an asymptotic theory for estimation based on signed ranks in the ARMA model when the innovation density is symmetrical. We provide two classes of estimators and we establish their asymptotic normality with the help of the asymptotic properties for serial signed rank statistics. Finally, we compare our procedure to the one of least-squares, and we illustrate the performa...

Precise identification of the time when a process has changed enables process engineers to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for a Poisson process in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step < /div> change, a linear trend and a known multip...

A video coding scheme is presented in which the coding is performed on individual moving objects. A Markov Random Field model is employed in nding the motion and boundaries of the objects. By guiding the object segmentation process with the spatial color information , meaningful objects representative of the real video scene are extracted. Furthermore, this enables a systematic treatment in han...

Biologically focused, agent-based models need many parameters in order to simulate system dynamics. It is often essential to explore the consequences of many parameter vectors before satisfactorily representing phenomena. In this work we propose a simple algorithm based on fuzzy clustering to estimate model parameter values for new situations utilizing the characteristics of previously simulate...