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Section: Partnerships and Cooperations
European Initiatives
FP7 Projects
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Coordinator: Sergey Kornienko and Paul Levi, Stuttgart University (Germany).
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Partners: Universität Stuttgart (USTUTT), Universität Graz (IZG), Vrije Universiteit (VU), Universität Karlsruhe (UNIKARL), Flanders Institute for Biotechnology (VIB), University of the West of England, Bristol (UWE), Eberhard Karls Universität Tübingen (UT), University of York (UY), Université Libre de Bruxelles (CENOLI), and Inria-TAO.
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Abstract: SYMBRION, an FP7 IP (Integrated Project), involving 10 partners from Robotics (Electronics and Mechanics), Evolutionary Biology, and Computer Science (working on bio-inspired complex systems). Integrating hardware and software design, Symbrion IP aims at designing autonomous swarm robots. The software will involve both time-scales of evolutionary learning and on-line learning, in direct connection with TAO research themes.
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Program: Design of a decision support tool for sustainable, reliable and cost-effective energy strategies in cities and industrial complexes
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Other Partners: AIT (Austria), INESC Porto (Portugal), ARMINES (France), Schneider Electric SAS (France), Comune di Cesena (Italy), Comune di Bologna (Italy), TUPRAS (Turkey), ERVET (Italy)
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Abstract: The overall objective of CitInES is to design and demonstrate a multi-scale multi-energy decision-making tool to optimise the energy efficiency of cities or large industrial complexes by enabling them to define sustainable, reliable and cost-effective long-term energy strategies. Demonstrations will take place in two cities in Italy, Cesena and Bologna, and in one oil refinery in Turkey, Tupras. Innovative energy system modelling and optimization algorithms will be designed to allow end-users to optimize their energy strategy through detailed simulations of local energy production, storage, transport, distribution and consumption, including demand side management and coordination functionalities enabled by smart grid technologies. All energy vectors (electricity, gas, heat...), usages (heating, air conditioning, lighting, transportation...) and sectors (residential, industrial, tertiary, urban infrastructure) will be considered to draw a holistic map of the city/industry energy behaviour. Energy strategy analyses will encompass advanced long-term risk analysis. As economic and technical situations are constantly evolving, a relevant energy strategy should be robust to different prospective scenarios. Hence, a diversified energy portfolio will allow city and industry authorities to react more efficiently to fuel price stresses and to decrease their exposition to a given energy solution. The expected impacts on end-users are threefold : 1) to assess the economic and environmental impacts of urban planning scenarios in terms of energy; 2) to optimise their local energy strategy to cost-effectively reduce CO2 emissions, including usage of local renewable energies, electric mobility integration, multi-energy coordination, smart grid integration and demand-side management; and 3) to assess financial and environmental long-term risks and propose robust energy schemes to face fuel and CO2 price uncertainties. The developed software will also be used as a communication tool for end-users to facilitate consultations between actors and to promote local authority decisions towards citizens. CitInES methodology will be demonstrated by optimizing long-term energy strategies for the two partner cities and for the partner oil refinery. The proposed strategies will be assessed and compared to initial end-user strategies to measure energy and CO2 emission savings.
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Program: Collaborative Project and Coordination and Support Action (CP-CSA)
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Other Partners: 40 in Europe and 8 more worldwide (details on http://www.egi.eu )
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Abstract: Collaborative effort involving more than 50 institutions in over 40 countries. Its mission is to establish a sustainable European Grid Infrastructure (EGI). EGI-InSPIRE is ideally placed to join together the new Distributed Computing Infrastructures (DCIs) such as clouds, supercomputing networks and desktop grids, for the benefit of user communities within the European Research Area.
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Program: Pattern Analysis, Statistical Modelling and Computational Learning
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Objective: PASCAL is a Network of Excellence funded by the European Union. It has established a distributed institute that brings together researchers and students across Europe, and is now reaching out to countries all over the world.
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Coordinator: John Shawe-Taylor, (Scientific coordinator), University College London, UK and Steve Gunn (Operational), University of Southampton, UK
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Abstract: PASCAL is developing the expertise and scientific results that will help create new technologies such as intelligent interfaces and adaptive cognitive systems. To achieve this, it supports and encourages collaboration between experts in Machine Learning, Statistics and Optimization. It also promotes the use of Machine Learning in many relevant application domains such as Machine vision, Speech, Haptics, Brain-Computer Interface, User-modeling for computer human interaction, Multimodal integration, Natural Language Processing, Information Retrieval, Textual Information Access.
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Program: Investigation of the design of complex learning systems to increase the performance of artificial intelligence
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Coordinator: Idiap Research Institute (Martigny, Switzerland)
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Other Partners: Heudiasyc laboratory (CNRS and UTC, Compiègne, France), University of Potsdam (Germany), Center for Machine Perception of the Czech Technical University, Pragua.
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Abstract: The goal of the MASH project is to create new tools for the collaborative development of large families of feature extractors. It aims at starting a new generation of learning software with great prior model complexity. The project is structured around this web platform. It comprises collaborative tools, such as a wiki-based documentation and a forum, and an experiment center to run and analyze experiments continuously. The applications targeted by the project are classical vision problems, and goal-planning in a 3D video game and with a real robotic arm. The scientific issues to be tackled along the course of the project are numerous, from standard Machine Learning questions such as learning and prediction with very large feature spaces and tight computational constraints, to original problems related to clustering in a functional space.
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Collaborations in European Programs, except FP7
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Project title: Energy efficiency in large scale distributed systems
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Other partners: see http://www.cost804.org .
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Abstract: The COST Action IC0804 proposes realistic energy-efficient alternate solutions to share IT distributed resources. While much effort is nowadays put into hardware specific solutions to lower energy consumptions, a complementary approach is necessary at the distributed system level, i.e., middleware, network and applications. The Action characterizes the energy consumption and energy efficiencies of these components.