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The present work is focused on the synthesis and the analysis of robust control techniques for rear electric traction control in 4×4 hybrid-converted CVs (Conventional Vehicles) at urban speed limits (lower than 60 Km/h). This set represents a practicable alternative for the automotive industry, improving vehicular performance and reducing considerably fossil fuel air pollution. Our goal is to design...
In this work we present an approach that uses a neural net for an online control of the cooling process in light metal die casting industry. Normally the die casting process is controlled manually or semi-manually, and quality control is done well after the cooling process. In our approach we increase the product quality during the production process by monitoring the cooling process with an infra...
We compare two approaches for misbehavior detection in sensor wireless networks based on artificial immune systems (AIS) and neural networks (NN). We conclude that AIS and NN based misbehavior detection offers a decent detection performance at a very low computational cost. However both approaches are different regarding the length of the preprocessing phase, memory requirements, speed of computation...
In this work we evaluate the feasibility of both classical machine learning algorithms and bio-inspired algorithms for misbehavior detection in sensor networks, since recent works in that field seem to concentrate mainly on bio-inspired approaches, without a convincing rational reason. As a first step, we analyze the packet traffic of a simulated sensor network in order to find relevant features that...
In this paper initial studies of the application of a hybrid model using artificial neural networks and conventional numerical methods to predict – as an example – twodimensional, isothermal, steady flow fields is presented. Main topics of the work were to show the principal possibility of using ANN in fluid mechanics and, additionally, to realize the potential of incorporation a priori knowledge...
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