Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Cyber Resilience of Micro-Grids (CRMG)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Standard European Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: United Kingdom
Call: Individual Fellowships (H2020-MSCA-IF-2020, 2020)
Start date: 2021-10-01
End date: 2023-09-30
Duration (months): 24.0
Total project cost (EUR): 224933.76
EU funding (EUR): 224933.76
Participants: 1
Project status: CLOSED

The data manipulation and disruption in the energy service and control process will damage the stable and secure operation of the microgrid (MG) systems. This proposal, cyber resilience of MGs (CRMG), investigates this problem from the system and control perspective. In particular, the MGs with distributed secondary control challenged by communication latency and denial-of-service (DoS) attacks will be studied. The objective is to provide holistic analysis and countermeasures for MGs under this cyber-attack scenario. The system model will be developed and analysed, while a model-based resilient framework to mitigate the influence of cyber-attacks will be provided. Besides, a data-driven cyber-resilient control based on deep reinforcement learning (DRL) will be proposed to provide online optimal control of MGs under unknown attack scenarios. The controller hardware-in-the-loop (HIL) experiment will be conducted to provide a realistic validation of the proposed method. The CRMG project will bridge the research limitations and provide solutions for cyber-resilience enhancement of MGs. The proposed research is important and timely, as the cyber-resilience of the electricity grid is an emerging and urgent concern for many European countries. The rapid development of renewable energy and IoT technologies transform the traditional power systems into distributed power and energy systems. The analysis and countermeasures of MGs under cyber-attacks will be investigated from system and control perspective, which can create both scientific and practical values. A software package with the proposed control tools will be provided, which includes a resilient framework to protect the MG from communication malfunctions by attackers, and a data-driven controller to enhance the system resilience. The research outcome would contribute to the cyber-resilience of the electricity grid and future MG development in Europe.

Keywords: micro grids,secondary control,distributed control,cyber physical system,cyber resilience,deep reinforcement learning
The Impact of Cohesion Policy on EU Identification (COHESIFY)

Framework: Horizon 2020 — Societal Challenges
Programme: Europe in a changing world - inclusive, innovative and reflective societies
Action type: Research and Innovation actions
Countries involved: Germany,Ireland,Italy,Spain,Cyprus,Hungary,Poland,Belgium,Netherlands,United Kingdom
Call: European cohesion, regional and urban policies and the perceptions of Europe (H2020-REFLECTIVE-SOCIETY-2015, 2015)
Start date: 2016-02-01
End date: 2018-04-30
Duration (months): 27.0
Total project cost (EUR): 2446300.0
EU funding (EUR): 2446300.0
Participants: 10
Project status: CLOSED

COHESIFY will assess the contribution of Cohesion policy to citizens’ identification with the EU and produce a new strategy to communicate EU Cohesion policy to citizens. It will assess how and to what extent EU Cohesion policy affects citizens’ perceptions of and identification with the European Union. The project will focus on three inter-related issues: (1)the identity(ies)of people in EU regions in EU, national, regional and local contexts; (2)the governance, communication and impacts of Cohesion policy, including citizens’ perceptions of the policy and identification with the EU, and (3)what is needed to make Cohesion policy more effective in terms of people’s perceptions of the policy and the EU more generally. A mixed-methods design will be adopted to study the relationship between Cohesion policy, policy performance and attitudes to the EU, distinguishing between different levels of governance and types of actors-from those involved in programme design and implementation to final beneficiaries and the wider public. The communication channels of Cohesion policy influence will be examined, distinguishing between public communication and political and social communication. COHESIFY will fill a key gap in knowledge for EU and national/regional policymakers and interested stakeholders. The outputs will inform Cohesion policy communication strategies at the EU, Member State and regional levels, enabling communication efforts to adapt to local and regional realities to maximise their impact - to increase the civic appreciation of Cohesion policy and to improve regional policy relevance and efficiency. The Consortium comprises academic institutions and SMEs across a range of EU Member States with complementary disciplinary backgrounds of understanding EU Cohesion policy, complemented by applied SME expertise in communication strategies and tools for engaging with citizens and the various political structures at EU, national, regional and local levels.

Keywords: cohesion policy,european identity,communication
PEROVSKITE OPTOELECTRONICS (PEOPLE)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Research and innovation staff exchange
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: Italy,China,Sweden,United Kingdom
Call: Marie Skłodowska-Curie Research and Innovation Staff Exchange (RISE) (H2020-MSCA-RISE-2015, 2015)
Start date: 2016-01-01
End date: 2019-12-31
Duration (months): 48.0
Total project cost (EUR): 1233000.0
EU funding (EUR): 823500.0
Participants: 5
Project status: CLOSED

The goal of this project is to develop a lasting collaboration between top class research teams in Europe and China that fosters progress in the broad area of perovskite optoelectronics through progress in materials science, chemistry, device physics, photophysics, and device engineering. This will be realized via a cross-European, trans-continental network. Such a network is required as perovskite opotoelectronics is a research area that requires strong collaborations between researchers with different background. More specifically, the PEOPLE network is intended as a key driver for the development of perovskite optoelectronics. Multiple disciplines need to be involved so that PEOPLE can cover the entire knowledge chain: the design/synthesis of perovskite materials, processing/characterization of perovskite films, photovoltaic applications of perovskites, other emerging optoelectronic devices based on perovskites, device physics and photophysics. The combined expertise in PEOPLE is essential to achieve a transformative impact. This project regroups leading research teams in the domain of perovskite optoelectronics, with complementary expertise, in an attempt to synergise the research effort in this area. The most important results from this project will be the human resources generated. This project will allow the exchange of ideas between top class researchers in Europe and China, both at a senior scientist level and most importantly at a junior level. Maintaining this ambition to educate researchers in a highly multidisciplinary fashion, in cutting edge techniques, is a central goal of this project. It is expected that the recognition and training of young talent in the development of perovskite optoelectronics will have long lasting benefits both for fundamental research and industry in Europe.

Keywords: perovskites,optoelectronics,solar cells,light emitting diodes,transistors
Developing Machine Learning Classifier Models for Eye Movements to Diagnose Major Psychiatric Disorders (SACCSCAN-IA-ML)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: Coordination and support actions
Countries involved: United Kingdom
Call: European SME innovation Associate - pilot (H2020-INNOSUP-02-2016, 2016)
Start date: 2017-09-29
End date: 2018-09-28
Duration (months): 12.0
Total project cost (EUR): 121250.0
EU funding (EUR): 121250.0
Participants: 1
Project status: CLOSED

1 in 5 of us are affected by mental illness during our lifetime. It is the largest cause of disability worldwide costing the global economy a staggering US$2.5 trillion per annum and rising to US$6 trillion per annum by 2030. Although illness specific treatments are available, doctors are struggling to provide accurate and timely diagnosis with misdiagnosis occurring more than half the time. Currently, when patients’ history, symptoms and behaviour don’t meet the criteria set out in the diagnostic manual, it may take up to 10 years to diagnose the illness. And unlike in other branches of medicine there are no objective tests in psychiatry to aid the diagnostic process. SaccScan is a novel point-of-care (PoC) software diagnostic system which has been demonstrated to detect schizophrenia with better than 95% accuracy and can be extended with the same precision to other major psychiatric conditions. The software diagnostic tool successfully utilises eye-movement abnormalities as clinical diagnostic biomarkers for serious mental illnesses. The test can be performed within 30 minutes in a standard consulting room environment and results produced over the internet at near realtime speed. Early economic modelling showed that introducing SaccScan into health care services could produce savings of €33 474 per patient in the case of suspected schizophrenia. The purpose of this SME Innovation Associate application is to identify a post-doctoral talent from across the EU to help develop next generation models within the SaccScan diagnostic system in order to increase the clinical utilities of the test by the identification and implementation of new eye movement biomarkers which in turn will extend the test to other illness categories.

Keywords: machine learning,eye movements,software diagnostics tool,ce marking,mental health
The Violence Early-Warning System: Building a Scientific Foundation for Conflict Forecasting (ViEWS)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: Institutions, Governance and Legal Systems
Panel description: Political science, international relations, law
Countries involved: Sweden,Norway
Call: ERC Advanced Grant (ERC-2015-AdG, 2015)
Start date: 2017-01-01
End date: 2022-06-30
Duration (months): 66.0
Total project cost (EUR): 2496972.0
EU funding (EUR): 2375718.25
Participants: 2
Project status: CLOSED

The challenges of preventing, mitigating, and adapting to large-scale political violence are daunting, particularly when violence escalates where it is not expected. Early-warning systems of sufficient quality and transparency do not exist, limiting the ability of the international community to effectively assist affected populations. We propose to develop, test, and iteratively improve a pilot Violence Early-Warning System (ViEWS) that is rigorous, data-based, and publicly available to researchers and the international community. This objective is challenging but feasible. The conflict research community has laid the ground for such a system through careful isolation of theoretically manageable sub-components of complex phenomena, and concomitant systematic, disaggregated data collection efforts. A major innovation in the project is to integrate these isolated research programs into a theoretically and methodologically consistent forecasting system, by means of dynamic simulation techniques in combination with Bayesian Model Averaging, and guided by continuous out-of-sample evaluation. This integration effort will not only allow an early-warning system of unprecedented scope and performance, but also build theoretically informative bridges between numerous compartmentalized conflict research programs. Concentrating on theoretical and methodological development, the pilot will be limited in scope to Africa but be scalable. ViEWS will provide early warnings for four forms of political violence: armed conflict involving states and rebel groups, armed conflict between non-state actors, violence against civilians, and forced population displacement, and apply these to specific actors, sub-national geographical units, and countries. Led by Håvard Hegre, the system will leverage the data resources within the Uppsala Conflict Data program, the world-leading provider of conflict data, in combination with a strong team of highly experienced conflict scholars.
Accelerators Validating Antimatter physics (AVA)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Innovative Training Networks (ITN)-European Training Networks
Action type: Marie Skłodowska-Curie actions
Panel: Physics
Countries involved: Germany,Austria,Germany,Germany,Germany,Germany,Hungary,Switzerland,Slovenia,France,Czechia,Denmark,United Kingdom,United Kingdom,Japan,United Kingdom
Call: Innovative Training Networks (H2020-MSCA-ITN-2016, 2016)
Start date: 2017-01-01
End date: 2021-02-28
Duration (months): 50.0
Total project cost (EUR): 3848854.18
EU funding (EUR): 3848854.1799999997
Participants: 25
Project status: CLOSED

Antiprotons, stored and cooled at low energies in a storage ring or at rest in traps, are highly desirable for the investigation of basic questions on fundamental interactions, the static structure of antiprotonic atoms, CPT tests by high-resolution spectroscopy on antihydrogen, as well as gravity experiments. Antimatter experiments are at the cutting edge of science. They are, however, very difficult to realize and have been limited by the performance of the only existing facility in the world, the Antiproton Decelerator (AD) at CERN. The Extra Low Energy Antiproton ring (ELENA) will be a critical upgrade to this unique facility and commissioned from summer 2016. This will significantly enhance the beam quality and enable new experiments. To fully exploit the discovery potential of this facility and to pave the way for a vibrant long-term physics program with low energy antiprotons, advances are urgently required in numerical tools that can adequately model beam transport, life time and interaction, beam diagnostics tools and detectors that can fully characterize the beam’s properties, as well as in into advanced experimental techniques for improved precision and novel experiments that exploit the enhanced beam quality that ELENA will provide. AVA is a new European training network between universities, research centers and industry that will carry out an interdisciplinary and cross-sector antimatter research and training program for a cohort of 15 Fellows. It targets new scientific and technical developments and aims at boosting the career prospects of all trainees.

Keywords: low energy antimatter physics,antiproton,positron,spectroscopy,ion trap,cooled beams,stored beams,cpt theory,precision studies,fundamental symmetries,antihydrogen
Intelligent active protection system for motorcyclists: saving lives through optimum use of latest advances in sensor technologies, wireless communication, Smartphone platforms, real-time analysis. (INE IAPS)

Framework: Horizon 2020 — Industrial Leadership
Programme: Information and Communication Technologies
Action type: SME instrument
Countries involved: France
Call: Open Disruptive Innovation Scheme (implemented through the SME instrument) (H2020-SMEINST-1-2014, 2014)
Start date: 2015-05-01
End date: 2015-10-31
Duration (months): 6.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

The risk of fatalities or sustaining serious injuries in motorcycling accidents is poorly addressed by the existing body protection devices available to that community. They lack in reactivity characteristics and offer limited protection to the most affected parts of the body. Combining advances in sensor technologies, wireless communication and Smartphone platforms in particular, In&motion is set to revolutionise the market with their intelligent active protection system. With an ultra-high energy absorption capacity and a much wider protection area, their all-electronic system further differentiates itself from competitor products with its auto-learning capability: the more it is used the better it performs. With a business-to-business approach, In&motion joins forces with key European motorcycling garments manufacturers who recognise the need for their specialised clothes to become intelligent and highly protective. The technology is therefore fully integrated into the clothing, making it very convenient for the user. To further simplify the user’s experience, In&motion’s other innovation lies in the ability for the user to reactivate the device after deployment, thanks to easy-to-handle consumable parts. This provides a significant step change in accessibility, dispensing and affordability for the user. With European motoring legislations set to further impact the market potential for body-protection devices, In&motion’s solution is bound to be truly disruptive. The technology has already been highly acclaimed by ski athletes and the International Ski Federation, and In&motion have high ambitions to see that technology also used in other areas such as drone protection, senior’s health and well-being, and work at height.

Keywords: airbag motorcycling intelligent active personal protection accident life saving sensors algorithm real time wireless communication smartphone skiing drone feedback loop
LEArning-CONtrol tight interaction: a novel approach to robust execution of mobile manipulation tasks (LEACON)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Standard European Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: Germany
Call: Marie Skłodowska-Curie Individual Fellowships (IF-EF) (H2020-MSCA-IF-2014, 2014)
Start date: 2016-03-07
End date: 2018-03-06
Duration (months): 24.0
Total project cost (EUR): 159460.8
EU funding (EUR): 159460.8
Participants: 1
Project status: CLOSED

One of the main challenges of roboticists is to take robots out of the factories and let them enter into unstructured environments, such as houses, hospitals, small manufacturers and dangerous area. The objective of the project is to take a step towards the presence of robots in such environments. Currently, there are still important obstacles to the massive diffusion of advanced mobile manipulation systems in the fields described above. First of all, programming mobile manipulators with the classical methods is still too expensive and time-consuming due to intrinsic complexity of mobile manipulation tasks. A second limitation is that planning the robot motion completely off-line, as often happens in classical industrial applications, may likely bring to a failure of the assigned task, since a high degree of uncertainty is present and the environment can dynamically change. Such features may cause safety issues for humans potentially present in the workspace and for the external environment itself. In order to tackle these limitations, the LEACON project has the objective to develop a framework that: - allows robots to learn in a real world scenario manipulation skills from human demonstration -exploits multimodal perception (tactile, proximity, visual, force sensors) to increase the robustness to unforeseen events and safety when manipulation tasks are executed. To fulfill such objectives, a multidisciplinary approach that combines machine learning and perception-based control is proposed. The core of the proposed framework will provide two planning levels tightly connected: the high-level and low-level cognitive system. To show the effectiveness of the developed architecture, the main use cases will be constituted by a robot that performs picking, manipulation, and placing operations in a dynamic, unstructured environment in presence of humans in its workspace. At the end of the project, the developed software will be released as open source code.
Enabling carbon accounting of trees on farms for agroforestry-based climate action (TREES4CLIMA)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Global Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Environment and Geosciences
Countries involved: United States,Denmark
Call: Individual Fellowships (H2020-MSCA-IF-2019, 2019)
Start date: 2021-09-01
End date: 2024-08-31
Duration (months): 36.0
Total project cost (EUR): 286921.92
EU funding (EUR): 286921.92
Participants: 2
Project status: SIGNED

Agriculture is a major driver of deforestation and greenhouse gas (GHG) emissions. Meeting the increasing demand for land and agricultural products, without compromising the Sustainable Development Goals (SDGs) agenda, will require a sustainable transformation of agricultural systems. Among the solutions, agroforestry systems which integrate trees, crops and livestock on farms, without requiring additional agricultural land, provide adaptation and mitigation to climate change and improve livelihoods. Despite its benefits, agroforestry is often not accounted for in measurement, reporting and verification (MRV) systems, which are a requirement for fulfilling countries’ climate goals under the Paris Agreement. If agroforestry is not included, incentives for leveraging on-farm trees for addressing climate change will be limited. Three scientific and technical gaps prevent the access of agroforestry to climate-finance: 1) Lack of accessible approaches for the representation of agroforestry in MRV systems; 2) Shortfall of data and tailored methods for carbon accounting in agroforestry systems; 3) Institutional barriers that withhold agroforestry from access to MRV and climate finance. The aim of this project is to develop and field-test robust, cost-effective approaches to account for carbon in agroforestry systems and enable innovative environments for climate finance. Three working packages (WP) will contribute to fill the existing gaps: WP1) Accessible detection, classification and representation of agroforestry systems; WP2) Development of methods for rapid, non-destructive quantification of carbon in agroforestry; WP3) Enablement of novel environments for agroforestry-based climate finance. The project outcomes will provide not only accessible tools and robust methods to facilitate carbon accounting in agroforestry, but also a link to climate finance. This combination will generate a multidisciplinary approach to catalyze agroforestry-based climate action.

Keywords: agroforestry,carbon sequestration,climate change,measurement,reporting and verification mrv systems,remote sensing,deep learning,machine learning,distributed ledger technologies dlts