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Block J.,Telvent
37th European Rotorcraft Forum 2011, ERF 2011

Safe helicopter operations require critical decisions at several levels within a helicopter enterprise. Government and company-imposed operating specifications such as AO21 (US) and JAR OPS III (EU) require that pilots, dispatchers, and communications center coordinators have detailed knowledge of weather conditions at a helicopter's origin, destination, and along the route of flight. Use of a single, coordinated weather information platform, assuring that pilots, ground crew, and flight control personnel all share the same, real-time weather information on which their 'go/no go' decisions are based, is critical to safe operations. The reliability of weather information can differ widely from provider to provider. Free internet sources of weather information are often unsuitable for aviation purposes, although it can be hard to discern this without doing a careful evaluation. Using a single source of weather information that is QICP certified insures that the weather information used is current and reliable enough for safe flight operations. Use of a checklist, such as the FlightRisk tool, can enhance the safety of helicopter operations by ensuring that pilots perform a consistent and routine check of weather that is independently and objectively evaluated. Finally, use of advanced monitoring and warning technologies such as Site Watch and RotorWatch can further enhance safety, by extending weather information to the in-flight phase of operations. The automated and continuous monitoring of all flights for all adverse weather conditions insures that weather will not be a factor impacting safe helicopter operations. Source

Larrazabal J.M.,Telvent | Penas M.S.,Complutense University of Madrid
Expert Systems with Applications

Trajectory tracking control for unmanned marine surface vessels (USV) is quite complex because of the strongly non-linearity of the system and the presence of uncertainties and environmental changing conditions. To face those issues, in this paper intelligent and conventional strategies are used as the main control framework for the rudder angle of an USV. The guidance law calculates the desired angle and estimates the trajectory based on the dynamic model of the autonomous ship, which has been generated from real data obtained from experiments with a scale prototype. The model accounts for the physical limitations of both the rudder and the ship propulsion system. An adaptive control law is first proposed which is suitable for any different trajectory and can deal with varying path shapes. This gain scheduling approach utilizes PID controllers whose tuning parameters have been optimized by genetic algorithms (GA) for the different operation points (GS-PID-GA). Besides, a fuzzy logic controller (FLC) is designed to deal with the uncertainties of the dynamics and to include the expertise of an operator. Simulations validate the effectiveness of the proposed control approaches that have been compared with conventional control. © 2016 Elsevier Ltd. All rights reserved. Source

Vazquez J.M.M.,Telvent | Ramirez J.A.O.,University of Seville | Gonzalez-Abril L.,University of Seville | Morente F.V.,University of Seville
Neural Computing and Applications

In this paper, Bayesian network (BN) and ant colony optimization (ACO) techniques are combined in order to find the best path through a graph representing all available itineraries to acquire a professional competence. The combination of these methods allows us to design a dynamic learning path, useful in a rapidly changing world. One of the most important advances in this work, apart from the variable amount of pheromones, is the automatic processing of the learning graph. This processing is carried out by the learning management system and helps towards understanding the learning process as a competence-oriented itinerary instead of a stand-alone course. The amount of pheromones is calculated by taking into account the results acquired in the last completed course in relation to the minimum score required and by feeding this into the learning tree in order to obtain a relative impact on the path taken by the student. A BN is used to predict the probability of success, by taking historical data and student profiles into account. Usually, these profiles are defined beforehand; however, in our approach, some characteristics of these profiles, such as the level of knowledge, are classified automatically through supervised and/or unsupervised learning. By using ACO and BN, a fitness function, responsible for automatically selecting the next course in the learning graph, is defined. This is done by generating a path which maximizes the probability of each user's success on the course. Therefore, the path can change in order to adapt itself to learners' preferences and needs, by taking into account the pedagogical weight of each learning unit and the social behaviour of the system. © 2011 Springer-Verlag London Limited. Source

Bouzid A.,Angel.com | Ma W.,Telvent

Interactive Voice Response (IVR) system which has successfully evolved into a big system is discussed. Currently deployed IVRs are generally not viewed as empowering tools, or ones that can effectively serve caller needs. The users tend to perceive them as obstacles installed by companies to keep callers from reaching expensive human agents. The viscerally strong reaction that callers have to IVRs is fully justified. They treat us with little intelligence and thoughtfulness and exhibit an unsettling degree of irrationality that breeds contempt, if not revulsion, against them. Simply that IVR technology is here to stay. IVR can do the job at a lower cost, more quickly, and with less effort on the part of the end user than any of the most cutting-edge communication technologies out there. Source

Bermejo Munoz J.,Telvent | Galan S.G.,University of Jaen | Lopez L.R.,University of Jaen | Prado R.P.,University of Jaen | And 3 more authors.
IEEE International Conference on Emerging Technologies and Factory Automation, ETFA

While the capability for growing on demand is the design foundation of the new generation of software platforms, physical systems always have limited resources. Therefore, there is a need to optimise the existing infrastructures and evolving them building on interoperability. The optimisation of the infrastructure entails not only addressing the computational and storage capabilities but also the network, its associated intelligence and the exchanged information. Therefore, the interoperability requirement in a large scale cyber-system spans from the lowest layers, interfacing with the physical resources, to the software building blocks, client devices and handled data. In this paper, several initiatives addressing interoperability among cloud IaaS layers, IaaS-PaaS, PaaS-SaaS, Cloud-Network and data are presented. © 2012 IEEE. Source

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