The EVA project (Electric Vehicle and Applications) aims to develop a consortium on the electro propulsion systems for electric and hybrid vehicles in the perspective to submit a proposal on the Green Vehicles calls of the Work Programme 2018-2020 “Transport” of H2020. Three partners among the four partners have already submitted a proposal on the call 2017 for a 12-partner project. Despite the marks greater than the thresholds, this proposal has not been funded by the European Commission. From this valuable experience, the three partners are working on a new proposal for the call 2018 with an adding partner with complementary skills. The EVA project is managed by the University of Lille, who will be the project leader for the proposal to the call LC-GV-02-2018 (Virtual product development and production of all types of electrified vehicles and components). The University of Lille has an expertise on simulation and energy management of electric and hybrid vehicles. The Vrije University of Brussels (Belgium) has an expertise on electrochemical batteries for electric and hybrid vehicles. The University of Cluj Napoca (Romania) has an expertise on electrical machines for electric and hybrid vehicles. The Typhoon HIL company (Serbia) has an expertise on embedded controller for testing and control of electric and hybrid vehicles. The consortium has thus relevant and complementary skills in the field of propulsion systems of electrified vehicles and their components. At the time, the consortium is looking for relevant partners for the targeted call in order to have a complete value chain. The industrial partners involved in the previous proposal have already been contacted (Renault, Valeo, Siemens Software, TUV). However, new partners are required to complete the consortium in agreement with the specificities of the new call, which include now hybrid vehicles and fuel-cell vehicle and not only battery electric vehicles. Moreover, based on the reviewer comments on the previous proposal, supplementary efforts will be realized to upgrade the quality of the proposal. First, contacts with leaders of other H2020 projects will be taken to better position the proposal with on-going projects, including the projects accepted in the last call. Second, a consulting company will be paid for the help in the proposal writing, specifically for an improvement of the “Impact” section, which is difficult to develop by scientific partners. Third, in complement with the classical meeting, a professional video-conference service will be booked in order to have weekly meeting the last months, without hazards of non-professional tools. Moreover, in a capitalization philosophy, the consortium will work after the submission on the call 2019 LC-GV-09-2020 (Next generation electrified vehicles for urban use). This call is not yet fully defined, but the topic is relevant for the consortium. Based on the partners of EVA, a new consortium will be developed in order to find the right partners for this second call.

Language is an ability that develops in young children through joint interaction with their caretakers and their physical environment. At this level, human language understanding could be referred as interpreting and expressing semantic concepts (e.g. objects, actions and relations) through what can be perceived (or inferred) from current context in the environment. Previous work in the field of artificial intelligence has failed to address the acquisition of such perceptually-grounded knowledge in virtual agents (avatars), mainly because of the lack of physical embodiment (ability to interact physically) and dialogue, communication skills (ability to interact verbally). We believe that robotic agents are more appropriate for this task, and that interaction is a so important aspect of human language learning and understanding that pragmatic knowledge (identifying or conveying intention) must be present to complement semantic knowledge. Through a developmental approach where knowledge grows in complexity while driven by multimodal experience and language interaction with a human, we propose an agent that will incorporate models of dialogues, human emotions and intentions as part of its decision-making process. This will lead anticipation and reaction not only based on its internal state (own goal and intention, perception of the environment), but also on the perceived state and intention of the human interactant. This will be possible through the development of advanced machine learning methods (combining developmental, deep and reinforcement learning) to handle large-scale multimodal inputs, besides leveraging state-of-the-art technological components involved in a language-based dialog system available within the consortium. Evaluations of learned skills and knowledge will be performed using an integrated architecture in a culinary use-case, and novel databases enabling research in grounded human language understanding will be released. IGLU will gather an interdisciplinary consortium composed of committed and experienced researchers in machine learning, neurosciences and cognitive sciences, developmental robotics, speech and language technologies, and multimodal/multimedia signal processing. We expect to have key impacts in the development of more interactive and adaptable systems sharing our environment in everyday life.

In this project, we intend to study the human-computer interaction in situated manner. We believe that the interaction must have a physical realization, anchored in the real world to be natural and effective. In order to embody interactive systems, we propose to use humanoid robots. Robots, endowed with perceptions, but also means to act in the environment, allow the integration of a physical context in the interaction for the machine as well as for humans. So it is a situated approach of man-machine dialogue that will prevail to the innovations brought by this project. To do so, three processes will interact. First, it will be necessary for the machine to build a context of interaction allowing making decisions about the future of the interaction. This environment will incorporate the spoken input provided by humans but also the perceptions of the environment and the robot’s proprioception. Based on this background, potentially uncertain because of errors introduced by the automatic analysis of speech, the machine will take decisions. To help humans to achieve effective interaction, the robot will produce an expressive feedback of its understanding of the context. The reconstruction of the context will be based of course on the recognition and understanding of the spoken inputs uttered by the user but not only. The originality of this project is to anchor the interaction in the physical world, in close link with the aim of this interaction. For example, as part of a collaborative task manipulation of objects, the context will incorporate information about the configuration of the objects from the perspective of the robot but also of the humans. Thus this project will continue the research conducted in the field of spatial reasoning, and especially perspective taking. Perspective taking is a process by which a machine adopts the viewpoint of another agent (human or artificial) to reason about what can be seen by one or the other. Thus, ambiguities in the context may be raised due to the physical impossibility of certain assumptions. Language processing methods, among others, are stochastic processes that provide hypotheses about the context of the interaction, associated with confidence levels indicating the degree of certainty on these assumptions. Decisions taken by the machine must consider the potentially generated ambiguities and keep track of them throughout the interaction. This issue is discussed in the community of man-machine dialogue, which includes Supélec and the LIA, through statistical optimization models of decision making processes but remains an important lock. The physical attitude that robots should adopt to make the interaction more natural and effective will be among the areas of research. Also, the voice of the robot, including the lexical content, but also the intonations will be investigated. Specifically, the Acapela group company will work on speech synthesis methods based on voice transforms.

Biomass burning aerosol (BBA) exerts a considerable impact on climate by impacting regional radiation budgets as it significantly reflects and absorbs sunlight, and its cloud nucleating properties perturb cloud microphysics and hence affect cloud radiative properties, precipitation and cloud lifetime. However, BBA is a complex and poorly understood aerosol species as it consists of a complex cocktail of organic carbon and inorganic compounds mixed with black carbon and hence large uncertainties exist in both the aerosol-radiation-interactions and aerosol-cloud-interactions, uncertainties that limit the ability of our current climate models to accurately reconstruct past climate and predict future climate change. The African continent is the largest global source of BBA (around 50% of global emissions) which is transported offshore over the underlying semi-permanent cloud decks making the SE Atlantic a regional hotspot for BBA concentrations. While global climate models agree that this is a regional hotspot, their results diverge dramatically when attempting to assess aerosol-radiation-interactions and aerosol-cloud-interactions. Hence the area presents a very stringent test for climate models which need to capture not only the aerosol geographic, vertical, absorption and scattering properties, but also the cloud geographic distribution, vertical extent and cloud reflectance properties. Similarly, in order to capture the aerosol-cloud-interactions adequately, the susceptibility of the clouds in background conditions; aerosol activation processes; uncertainty about where and when BBA aerosol is entrained into the marine bundary layer and the impact of such entrainment on the microphysical and radiative properties of the cloud result in a large uncertainty. BBA overlying cloud also causes biases in satellite retrievals of cloud properties which can cause erroneous representation of stratocumulus cloud brightness; this has been shown to cause biases in other areas of the word such as biases in precipitation in Brazil via poorly understood global teleconnection processes. It is timely to address these challenges as both measurement methods and high resolution model capabilities have developed rapidly over the last few years and are now sufficiently advanced that the processes and properties of BBA can be sufficiently constrained. This measurement/high resolution model combination can be used to challenge the representation of aerosol-radiation-interaction and aerosol-cloud-interaction in coarser resolution numerical weather prediction (NWP) and climate models. Previous measurements in the region are limited to the basic measurements made during SAFARI-2000 when the advanced measurements needed for constraining the complex cloud-aerosol-radiation had not been developed and high resolution modelling was in its infancy. We are therefore proposing a major consortium programme, CLARIFY-2016, a consortium of 5 university partners and the UK Met Office, which will deliver a suite of ground and aircraft measurements to measure, understand, evaluate and improve: a) the physical, chemical, optical and radiative properties of BBAs b) the physical properties of stratocumulus clouds c) the representation of aerosol-radiation interactions in weather and climate models d) the representation of aerosol-cloud interactions across a range of model scales. The main field experiment will take place during September 2016, based in Walvis Bay, Namibia. The UK large research aircraft (FAAM) will be used to measure in-situ and remotely sensed aerosol and cloud and properties while advanced radiometers on board the aircraft will measure aerosol and cloud radiative impacts. While the proposal has been written on a stand-alone basis, we are closely collaborating and coordinating with both the NASA ORACLES programme (5 NASA centres, 8 USA universities) and NSF-funded ONFIRE programme (22 USA institutes).