One of the factors hindering the wide-spread application of qualitative simulation techniques is the difficulty encountered when developing a qualitative model . Analyzing the resulting behavioral description and revising the model in response to this analysis requires a significant amount of expertise and is often left up to the modeler. As a result, developing a qualitative model is difficult...

A common task in business process modelling is the verification of process models regarding syntactical and structural errors. While the former might be checked with low efforts, the latter usually requires a complex state-space analysis to prove properties like deadlock-freedom of the models. In this paper, we address the issue of deadlock detection with a novel approach based on business proc...

This paper presents a comprehensive formulation of a linearized state space process model for a generic two-reactant-two-product reactive distillation system. The development of the model requires the knowledge of the desired steady state design data, including liquid holdups and composition profiles. The application of the developed linear process model in composition estimation and control of...

In sensor data management, it is often useful to collect statistics about state changes of an observed phenomenon. The most basic case is to keep track of how often certain transitions, e.g. from state c to d, have occurred. In our research, we consider a discrete state space and timeline. For example, in the above figure, the state space corresponds to actual two-dimensional space and is divid...

This paper proposes a class of algorithms for analyzing event count time series, based on state space modeling and Kalman filtering. While the dynamics model is kept Gaussian linear, nonlinear observation function chosen. In order to estimate states, an iterated extended filter employed. Positive definiteness covariance matrices preserved by square-root filtering approach, singular value decomp...