Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Artificial Intelligence beyond quality (TECHEYE)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Spain
Call: SME instrument (H2020-SMEInst-2018-2020-1, 2019)
Start date: 2019-12-01
End date: 2020-03-31
Duration (months): 4.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

In the automotive industry, quality is the key differentiating factor. Scraps and defects represent a major risk to performance and competitiveness, leading to very high costs. Besides, there is no specific solution in the market for the automatic quality control of defects in plastic injection and painting defects, forcing manufacturers to maintain primitive techniques based on human vision, that can be time-consuming and costly. By applying Artificial Intelligence (AI) to visual inspections, organisations can identify defects by matching patterns to images that were previously analysed and classified. They could be detected up to 90% more accurately than humans. In addition, reduction of personnel costs, rework, and scrap yields a total cost reduction of approximately 9% (~ €26 billion). A new market report from Market&Market claims that automotive AI will reach €9.37 billion by 2025, at a CAGR of 38.46%. TECHEYE® is an innovative solution offering a fully automated directional vision system coupled with a self-calibration lighting unit, based on Neural Networks. It comprises suitable hardware for images acquisition, a communications system and software for image processing and analysis. Our algorithms can automate visual inspection activities, even under complex conditions like variable position, orientation of units or tough lighting conditions. TECHEYE® is able to classify injection faults: gusts, ampoules, pores and scratches. We offer an affordable web service, user-friendly as plug and play, enabling defect classification. TECHEYE® can be really suitable for automotive OEMs and plastic injection companies to enhance their production lines, the quality assurance and cost reduction. INOVALABS is a technical consulting company founded in 2004 in Vigo (Spain), providing services related to the definition of innovation and competitiveness strategies, and design of R&D projects. TECHEYE® will boost the company revenues streams over a new market niche

Keywords: artificial intelligence,machine vision,machine learning,neural networks,deep learning,image,acquisition,quality control,inspection,vision system,computer vision,plastic injection
An innovative and sustainable process for reducing the environmental impact of stainless steel production, enabling impressive recovery rates and cost savings. (REGMAX)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Austria
Call: SME instrument (H2020-SMEInst-2018-2020-2, 2018)
Start date: 2018-06-01
End date: 2020-05-31
Duration (months): 24.0
Total project cost (EUR): 3525316.25
EU funding (EUR): 2467721.38
Participants: 1
Project status: CLOSED

Pickling is a metal surface treatment used in the stainless steel industry to remove impurities from steel and obtain optimal services. Every hour, approximately 300,000 litres of acid are used in pickling, resulting in huge quantities of waste acid. Alongside the damaging environmental impact, steel pickling works suffer from high operational costs to manage the disposal or regeneration of said acid. SUSTEC has developed the REGMAX system, a new acid regeneration process to provide a sustainable low-cost solution to this problem. The REGMAX system increases acid regeneration rate to over 97% and reduces operational costs by up to 91%. Within the overall project, SUSTEC intends to scale-up regeneration capacity; and commission a pilot plant for a large scale demonstration and validation of the technology in industrial conditions

Keywords: acid regeneration,waste acid,sustainable steel production,steel pickling,resource efficiency
Deep-Learning and HPC to Boost Biomedical Applications for Health (DeepHealth)

Framework: Horizon 2020 — Industrial Leadership
Programme: Information and Communication Technologies
Action type: Innovation actions
Countries involved: Germany,Spain,Italy,Italy,Spain,Germany,Spain,Italy,Italy,Germany,Spain,Spain,Switzerland,Sweden,Romania,Greece,France,France,Netherlands
Call: HPC and Big Data enabled Large-scale Test-beds and Applications (H2020-ICT-2018-2, 2018)
Start date: 2019-01-01
End date: 2022-06-30
Duration (months): 42.0
Total project cost (EUR): 14642324.02
EU funding (EUR): 12774824.8
Participants: 25
Project status: CLOSED

Health scientific discovery and innovation are expected to quickly move forward under the so called “fourth paradigm of science”, which relies on unifying the traditionally separated and heterogeneous high-performance computing and big data analytics environments. Under this paradigm, the DeepHealth project will provide HPC computing power at the service of biomedical applications; and apply Deep Learning (DL) techniques on large and complex biomedical datasets to support new and more efficient ways of diagnosis, monitoring and treatment of diseases. DeepHealth will develop a flexible and scalable framework for the HPC + Big Data environment, based on two new libraries: the European Distributed Deep Learning Library (EDDLL) and the European Computer Vision Library (ECVL). The framework will be validated in 14 use cases which will allow to train models and provide training data from different medical areas (migraine, dementia, depression, etc.). The resulting trained models, and the libraries, will be integrated and validated in 7 existing biomedical software platforms, which include: a) commercial platforms (e.g. PHILIPS Clinical Decision Support System from or THALES SIX PIAF; and b) research oriented platforms (e.g. CEA`s ExpressIF™ or CRS4`s Digital Pathology). Impact is measured by tracking the time-to-model-in-production (ttmip). Through this approach, DeepHealth will also standardise HPC resources to the needs of DL applications, and underpin the compatibility and uniformity on the set of tools used by medical staff and expert users. The final DeepHealth solution will be compatible with HPC infrastructures ranging from the ones in supercomputing centers to the ones in hospitals. DeepHealth involves 21 partners from 9 European Countries, gathering a multidisciplinary group from research organisations (9), health organisations (4) as well as (4) large and (4) SME industrial partners, with strong commitment towards innovation, exploitation and sustainability.

Keywords: deep learning,heterogeneous architectures
Connecting and Analysing the Digital Transport Ecosystem (CONNECTIVE)

Framework: Horizon 2020 — Societal Challenges
Programme: Smart, green and integrated transport
Theme: Public-private partnerships
Destination: European rail initiative
Action type: Innovation actions
Partnership: Shift2Rail (Art-187)
Countries involved: Germany,Spain,Italy,France,France,United Kingdom
Call: Technical framework for attractive railway services (H2020-S2RJU-CFM-2017, 2017)
Start date: 2017-09-01
End date: 2023-10-31
Duration (months): 74.0
Total project cost (EUR): 5025410.37
EU funding (EUR): 3513534.52
Participants: 11
Project status: SIGNED

CONNECTIVE project will be part of Shift2Rail (S2R), the first rail joint technology initiative focused on accelerating the integration of new and advanced technologies into innovative rail product solutions. CONNECTIVE aims to be the technical backbone of S2R´s Innovation Programme 4 (IP4), which addresses the provision of “IT solutions for attractive Railway services”. Thereby, CONNECTIVE will provide other S2R-IP4 projects with a technical framework and a set of tools that will foster the digital transformation of the rail and in general all the transport ecosystem, allowing to reach unprecedented multimodal travel experiences and to improve the fit between supply and demand. CONNECTIVE will provide new levels of interoperability and seamless access to all transport data and services in a multimodal and distributed environment, while offering a common business intelligence to extract insights of the ecosystem, valuable for both users and service providers.

Keywords: interoperability,business analytics,transport ecosystem,semantics
Data Science for Tapoi (DataSci4Tapoi)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: Coordination and support actions
Countries involved: Italy
Call: European SME innovation Associate - pilot (H2020-INNOSUP-02-2016, 2016)
Start date: 2017-09-01
End date: 2018-08-31
Duration (months): 12.0
Total project cost (EUR): 85250.0
EU funding (EUR): 85250.0
Participants: 1
Project status: CLOSED

Tapoi (http://www.tapoi.me/) is an innovative customer intelligence service for web portal owners and mobile application developers. Tapoi leverages online activities of users to provide actionable knowledge on their interests and attitudes, allowing the optimization of offers and contents and the improvement of the user experience. Tapoi semantically analyzes the collected activities understanding precisely the meaning of texts, urls, places and tags and uses such meaning to build rich and detailed user profiles describing the interests and preferences demonstrated by the users’ activities. The technological core of Tapoi is a profiling engine that can be specifically tailored to a vertical domain. This engine is able to collect the user activities from the social networks and to semantically analyze them in order to capture deep and detailed knowledge about their interests and preferences. While the customer feedback has been so far very good, and the product is considered to have achieved product-market fit, its current version presents a number of shortcomings which may harm growth and thereby prevent the applicant to fully exploit its business potential. This is related to the construction of models for specific domain, something which at the moment is carried out manually with the assistance of third-party subject matter expert, and the matching functionality which is bespoke and typically defined at design time (hence before the service becomes deployed). U-Hopper believes that advanced data science and machine learning approaches could be successfully applied to tackle the aforementioned issues, thereby providing a boost to the growth of the company and positioning it at the forefront of user analytics for web portals and mobile apps. The project aims at complementing the current team expertise by hiring a top-notch data scientist able to work with the Tapoi team to deliver a new, enhanced version of the product.

Keywords: data science,machine learning,big data,user profiling
Off-the-Shelf Polypeptide-based Immunotherapy for Advanced Melanoma Treatment (POLYMMUNE)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Proof of concept grants
Action type: ERC actions
Countries involved: Spain
Call: ERC Proof of Concept Grant (ERC-2018-PoC, 2018)
Start date: 2019-01-01
End date: 2020-09-30
Duration (months): 21.0
Total project cost (EUR): 149938.0
EU funding (EUR): 149938.0
Participants: 1
Project status: CLOSED

Despite various existing drug delivery methods, the key challenge remains: how to deliver drugs to the desired site of therapeutic action to achieve best treatment outcome, while minimising side effects? This is even more challenging in cancer immunotherapy as a complex interplay of immune reactions need to be activated. POLYMMUNE will evaluate the technical and commercial viability of our novel polymeric nanocarrier platform (developed during my ERC Consolidator grant MyNano) for targeted drug delivery of a wide variety of diseases and therapeutics. As proof of concept, we will focus on immunotherapy delivery for metastatic melanoma. Globally ~132,000 new melanoma cases will be diagnosed each year and despite recent successes, only 50% of the patients respond to novel immunotherapies that are costly, while causing severe side effects. Coupling a commercially available antigen against metastatic melanoma to our nanocarrier will activate a broad immune response, resulting in an effective cancer vaccine that potentially targets different melanoma types, enabling off-the-shelf production. This dual action will lead to increased clinical benefit, which will come with less side effects and will be 5-10-fold cheaper than current treatments. Additionally, our conjugate is industrially scalable and thus overcome a major bottle neck of current nanocarrier-based medicine. We envision our platform technology to be used as a method of choice for drug delivery in many medical applications, such as cancer immunotherapy and neurodegenerative diseases. These applications are within reach, as depending on the modification, our conjugates can e.g. bypass the blood brain barrier and potentially be administered intranasally (nanogel). Our platform offers a highly attractive business case, as biotechnology and pharmaceutical companies heavily invest in nanoconjugates due to the need for novel drug delivery strategies.

Keywords: polypeptide based carrier,polymer therapeutics,immunotherapy,advanced drug delivery,advanced melanoma
Powered, modular Wind-Tunnel model for low and high Reynolds tests (POLITE)

Framework: Horizon 2020 — Societal Challenges
Programme: Smart, green and integrated transport
Theme: Public-private partnerships
Destination: Clean Sky 2
Action type: Innovation actions
Partnership: CS2 (Art-187)
Countries involved: Italy,Germany,Switzerland,United Kingdom
Call: Powered WT model design and manufacturing of the FTB2 aircraft configuration for aerodynamic tests in wind tunnel at low and high Reynolds number (H2020-CS2-CFP02-2015-01, 2015)
Start date: 2016-10-01
End date: 2019-12-31
Duration (months): 39.0
Total project cost (EUR): 3932575.5
EU funding (EUR): 2841943.0
Participants: 4
Project status: CLOSED

POLITE answers the CfP JTI-CS2-2015-CFP02-REG-02-02, published in the second batch of the JTI-Cleansky 2. Within the work-package linked to CS2 a so-called flying test bed (FTB) is developed and used for analyzing and validating technologies developed within CS2, domain REG. This is clearly in line with Cleansky 2, which includes, among other topics, to deliver innovative technologies that are validated in relevant conditions (in this case flight test). Before such a complex test can be performed a high level of confidence in the technologies developed is necessary. POLITE answers this need, since it enables low and high Reynolds WT-tests that allow to validating technologies to improve performances in low and high speed. Increasing the aircraft performance is supporting not only the CS2-workplan but also the ACARE2020 vision by allowing passengers to travel with reduced fuel burn and there increasing the ecologic efficiency of the aircraft. Within POLITE a consortium build from 4 different partners will tackle all relevant topics. The consortium is build up in way that all necessary domains are covered. In particular three partners the consortium includes - ARA with a strong competence in model design activities and manufacturing - IBK with a good experience in developing large, specialised models - DREAM with a very good expertise in aerodynamics and model design in particular - RUAG involving the background on powered models and introducing the technology necessary for the engines. Their LWTE is the solution candidate for the low-Re tests. Additionally to this ONERA will be included via subcontracting. ONERA has a strong background in air-driven motors which are used in the pressurized WT ONERA F1. They will introduce this technology into the consortium for implementation into the model used for high-Re tests.

Keywords: powered wt model,pressurized tests,low speed test,aerodynamic efficiency
HPC and Big Data Technologies for Global Systems (HiDALGO)

Framework: Horizon 2020 — Excellent Science
Programme: Research Infrastructures
Theme: Research infrastructures
Destination: European Data Infrastructure
Action type: Research and Innovation actions
Partnership: HPC PPP (cPPP)
Countries involved: Austria,Germany,Spain,Austria,Germany,Hungary,Hungary,Greece,Poland,United Kingdom,United Kingdom
Call: HPC PPP - Centres of Excellence on HPC (H2020-INFRAEDI-2018-1, 2018)
Start date: 2018-12-01
End date: 2022-02-28
Duration (months): 39.0
Total project cost (EUR): 7991500.0
EU funding (EUR): 7991500.0
Participants: 14
Project status: CLOSED

Developing evidence and understanding concerning Global Challenges and their underlying parameters is rapidly becoming a vital challenge for modern societies. Various examples, such as health care, the transition of green technologies or the evolution of the global climate up to hazards and stress tests for the financial sector demonstrate the complexity of the involved systems and underpin their interdisciplinary as well as their globality. This becomes even more obvious if coupled systems are considered: problem statements and their corresponding parameters are dependent on each other, which results in interconnected simulations with a tremendous overall complexity. Although the process for bringing together the different communities has already started within the Centre of Excellence for Global Systems Science (CoeGSS), the importance of assisted decision making by addressing global, multi-dimensional problems is more important than ever. Global decisions with their dependencies cannot be based on incomplete problem assessments or gut feelings anymore, since impacts cannot be foreseen without an accurate problem representation and its systemic evolution. Therefore, HiDALGO bridges that shortcoming by enabling highly accurate simulations, data analytics and data visualisation but also by providing knowledge on how to integrate the various workflows and the corresponding data.

Keywords: hpc hpda technology exascale simulation bigdata analytics
Understanding Others and the Self - What does brain maturation tell us about early Theory of Mind development and its relation to the emergence of a self-concept in early childhood? (ToMSelfLink)

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: Social Sciences and Humanities
Countries involved: Denmark
Call: Individual Fellowships (H2020-MSCA-IF-2017, 2017)
Start date: 2018-05-01
End date: 2023-03-29
Duration (months): 32.0
Total project cost (EUR): 200194.8
EU funding (EUR): 200194.8
Participants: 1
Project status: TERMINATED

Human social interaction crucially depends on our ability to understand what others think or believe, an ability referred to as Theory of Mind (ToM). While decades of research assumed that this milestone is reached around the age of 4 years, recently, novel infant ToM tasks have shown that already infants younger than 2 years expect people to act according to their beliefs - even if these differ from the infant’s own beliefs. This has created one of the biggest puzzles of developmental psychology: Why do children consistently fail traditional ToM tasks until the age of 4 years, if already infants understand others' beliefs? The supervisor has recently suggested how the emergence of a self-concept (SELF) and ability to distinguish between self and other might build a link between early- and later-developing ToM abilities. Here, we want to address these questions with cognitive neuroscience, by combining behavioral and infant functional neuroimaging (fNIRS) tasks of ToM and SELF with brain-structural magnetic resonance imaging (MRI). This combination of methods will allow us to compare the brain regions involved in the novel infant and later-developing traditional ToM tasks, and to study their link to the brain regions involved in SELF. I have a strong background in combining brain-structural MRI with behavioral ToM tasks in preschool-aged children. My supervisor is a world-leading expert in early ToM and developing cutting-edge neurocognitive tasks for preverbal infants. She was recently awarded an ERC grant to test her theory on the role of SELF for ToM. The current action importantly complements that project by an innovative neuroimaging approach, which uniquely becomes possible through our combined expertise. I will strongly benefit from the infrastructure and expertise in that project, and will be able to learn cutting-edge infant neurocognitive testing methods, crucial for my future career goal of becoming a leading researcher in early-childhood development.

Keywords: theory of mind,mentalizing,self awareness,neurocognitive development,infant cognition,developmental psychology,social cognition
A LIFE JACKET FOR YOUR HOUSE IN FLOOD TIMES (FloodFrame)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Denmark
Call: SME instrument (H2020-SMEInst-2018-2020-1, 2019)
Start date: 2019-05-01
End date: 2019-08-31
Duration (months): 4.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

Climate change has increased the occurrence and frequency of flood events worldwide, and 215 million people are currently under flood risk. Flooding can be devastating for both homes and businesses, causing economic loss. An estimated cost of a flood is €98,000 per household, and, to date, there is a lack of effective individual solutions for houses and buildings in the flood protection market. At FloodFrame A/S (DK, 2017), we have developed FloodFrame (patent pending): an automatic and pre-installed Flood Protection Technology for houses and buildings based on a waterproof membrane, which wraps the building during flood events protecting also its foundation. It does not require a flat surface to be effective and is activated by the rising water functioning without any electricity or human interaction while giving the possibility to increase the protection height during the flood automatically as high the water rises. FloodFrame is currently offered at a competitive price of €300/m. Today there is no solution in the flood protection market that could fulfill the specific needs of home owners and at an affordable price. Our primary target customers are homeowners who have already experienced a flood, like our 12 Early Adopters in Roskilde Fjord and Houston (US). Today, our prototype is at TRL7, and this project will help us to reach the TRL9 by optimizing and testing the final design and the installation process as well as to complete the commercial activities still needed to reach the market (e.g. supply chain consolidation and fulfilment of legal and quality requirements). By 2024 and based on a two-fold business model – direct sales and license holders – we expect to reach at least 0.7% of the market across Europe and US (least 7,387 properties protected by FloodFrame).Thus, we have forecasted €29.72 million of cumulated revenues (€18.63 cumulative profit - ROI 4.45) and at least 40 new employees (20 technical support, 10 trainers, 4 engineers and 6 sales experts)

Keywords: flood protection system,automatic,building and houses,foundation,water triggered