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Grant
Agency: European Commission | Branch: FP7 | Program: CP | Phase: ICT-2013.4.2 | Award Amount: 3.92M | Year: 2014

The objective of AMIDST is to develop a toolbox providing a scalable framework that facilitates efficient analysis and prediction based on information captured in streaming data. The work includes developing and scaling up existing algorithms in order to make the AMIDST toolbox flexible and versatile enough as to cope with the needs and requirements of a wide variety of applications. The toolbox will be particularized to address three industrial use-cases. Each use-case solution will be used to rigorously test the framework on real and complex data.The consortium has a strong and balanced combination of research and industrial partners. The academic partners ensure a scientific approach to theoretical and methodological aspects of the project. The industrial partners illustrate the importance of the potential developments provided by AMIDST for the EU economy, as they represent four strategic EU areas: software development, automotive industry, energy, and finance. AMIDST will make significant contributions towards the expected impacts of the call objectives. It will provide a generic framework for analysis of extremely large volumes of streaming data, thereby adding, creating and increasing the value of existing and new data resources as well as providing a means for more timely and efficient decision making. Each use-case solution represents an important contribution to its application domain.The industrial and commercial involvement in AMIDST ensures a high degree of commercial exploitations of the solutions developed. Each use-case represents one domain of commercial exploitation of effective solutions whereas the general framework will be applicable to a wide range of other domains. With the objective of creating a strong positive synergy, AMIDST takes an integrated European approach and joins partners with high interests in probabilistic modeling methods as well as techniques and algorithms for analysis of extremely large data volumes.


Grant
Agency: European Commission | Branch: FP7 | Program: CP | Phase: ENV.2012.6.2-1 | Award Amount: 11.49M | Year: 2012

Despite improved understanding of the links between ecosystem health, provision of ecosystem services and human well-being, further conceptual and empirical work is needed to make the ideas of ecosystem services (ESS) and natural capital (NC) operational. OpenNESS will therefore develop innovative and practical ways of applying them in land, water and urban management: it will identify how, where and when the concepts can most effectively be applied to solve problems. To do this, it will work with public and private decision makers and stakeholders to better understand the range of policy and management problems faced in different case study contexts (ranging across locales, sectors, scales and time). OpenNESS will consolidate, refine and develop a range of spatially-explicit methods to identify, quantify and value ecosystem services, and will develop hybrid assessment methods. It will also explore the effectiveness of financial and governance mechanisms, such as payments for ecosystem services, habitat banking, biodiversity offsetting and land and ecosystem accounting. These types of interventions have potential for sustaining ESS and NC, and for the design of new economic and social investment opportunities. Finally, OpenNESS will assess how current regulatory frameworks and other institutional factors at EU and national levels enable or constrain consideration of ESS and NC, and identify the implications for issues related to well-being, governance and competitiveness. OpenNESS will analyse the knowledge that is needed to define ESS and NC in the legal, administrative and political contexts that are relevant to the EU. The work will deliver a menu of multi-scale solutions to be used in real life situations by stakeholders, practitioners, and decision makers in public and business organizations, by providing new frameworks, data-sets, methods and tools that are fit-for-purpose and sensitive to the plurality of decision-making contexts.


Grant
Agency: European Commission | Branch: FP7 | Program: CP-IP | Phase: FoF.NMP.2013-8 | Award Amount: 7.37M | Year: 2013

The vision of SelSus is to create a new paradigm for highly effective, self-healing production resources and systems to maximise their performance over longer life times through highly targeted and timely repair, renovation and up-grading. These next generation machines, fixtures and tools will embed extended sensory capabilities and smart materials combined with advanced ICT for self-diagnosis enabling them to become self-aware and supporting self-healing production systems. Distributed diagnostic and predictive repair and renovation models will be embedded into smart devices to early prognosis failure modes and component degradations. Self-aware devices will built on synergetic relationship with their human operators and maintenance personnel through continuous pro-active communication to achieve real self-healing systems. This will drastically improve the resilience and long term sustainability of highly complex manufacturing facilities to foreseen and unforeseen disturbances and deteriorations thereby minimising energy and resource consumption and waste. The SelSus vision will be achieved by the development of a new synergetic diagnostic and prognosis environment which is fully aware of the condition and history of all the machine components within a system or factory and is in constant knowledge enriched dialogue with their human personnel. The SelSus project will adopt a systematic approach, supported by a well-defined work plan. The work plan comprises nine carefully defined work packages. In order to guarantee fully committed teams towards comprising goals, the number of individual work packages is kept clearly constrained. The strong industrial pull for the project will be translated into a clear set of industrial requirements aimed at well-defined demonstration scenarios from the automotive and white goods industry.

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