Abstract
The penetration of micro-generation brings complex problems to the energy field. In this way, various simulators were designed to give decision support for the stakeholders, however, they intent to solve very specific problems. The proposed tool enables the interoperability between heterogeneous simulators, to simulate more complex problems.

Abstract
The development of vision-based navigation systems for mobile robotics applications in outdoor scenarios is a very challenging problem due to frequent changes in contrast and illumination, image blur, pixel noise, lack of image texture, low image overlap and other effects that lead to ambiguity in the interpretation of motion from image data. To mitigate the problems arising from multiple possible interpretations of the data in outdoor stereo egomotion, we present a fully probabilistic method denoted as probabilistic stereo egomotion transform. Our method is capable of computing 6-degree of freedom motion parameters solely based on probabilistic correspondences without the need to track or commit key point matches between two consecutive frames. The use of probabilistic correspondence methods allows to maintain several match hypothesis for each point, which is an advantage when ambiguous matches occur (which is the rule in image feature correspondence problems), because no commitment is made before analysing all image information. Experimental validation is performed in simulated and real outdoor scenarios in the presence of image noise and image blur. Comparison with other current state-of-the-art visual motion estimation method is also provided. Our method is capable of significant reduction of estimation errors mainly in harsh conditions of noise and blur.

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Abstract
Optimum operation of the energy consumption of end-users gains more importance to reduce total electricity bills and in order to more efficiently use energy resources thanks to smart grid concept. Electrical railway stations are one of the best places to take into account for in this manner. This study proposes a railway station energy management (RSEM) model. As the main contribution to the literature, regenerative braking energy (RBE) recovered during the operation of a metro line is assumed to meet the station load demand in daily operation. The RSEM model composed of RBE usage, energy storage system (ESS), and grid support is formulated as a mixed-integer linear programming (MILP) framework. RSEM model is tested by introducing the impact of passengers change on RBE for such cases that whether RBE and ESS are considered or neglected.

Abstract
One of the main challenges in power & energy systems is the development of decision support tools which approach the problem as a whole. In this scope, this work contributes to the increase of the interoperability between heterogeneous agent based systems through the use of ontologies, enabling semantic communications.