Integrated management of urban wastewater systems: a model-based approach

The main objective pursued in this thesis targets the development and systematization of a methodology that allows addressing management problems in the dynamic operation of Urban Wastewater Systems. The proposed methodology will suggest operational strategies that can improve the overall performance of the system under certain problematic situations through a model-based approach. The proposed methodology has three main steps: The first step includes the characterization and modeling of the case-study, the definition of scenarios, the evaluation criteria and the operational settings that can be manipulated to improve the system’s performance. In the second step, Monte Carlo simulations are launched to evaluate how the system performs for a wide range of operational settings combinations, and a global sensitivity analysis is conducted to rank the most influential operational settings. Finally, the third step consists on a screening methodology applying a multi-criteria analysis to select the best combinations of operational settings.

Model-based objects recognition in industrial environments for autonomous vehicles control

Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an image interpretation system intended to assist the navigation of autonomous vehicles that operate in industrial environments. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using a rule-based cooperative expert system

Model-based objects recognition in man-made environments

We describe a model-based objects recognition system which is part of an image interpretation system intended to assist autonomous vehicles navigation. The system is intended to operate in man-made environments. Behavior-based navigation of autonomous vehicles involves the recognition of navigable areas and the potential obstacles. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using CEES, the C++ embedded expert system shell developed in the Systems Engineering and Automatic Control Laboratory (University of Girona) as a specific rule-based problem solving tool. It has been especially conceived for supporting cooperative expert systems, and uses the object oriented programming paradigm