The COHERENCE4D project aims at developing a new paradigm for modeling, visualizing, interacting, and maintaining the coherence of 4D digital twins interfaced with physical systems that evolve over time. The work will focus on the definition of a model with variable geometry and topology over time, on the development of update mechanisms, on the definition of a new methodology for the specification and integration of acquisition interfaces, on the development of a decision support system exploiting an Artificial Intelligence capable to analyze the real/virtual gaps in order to decide on the updates to be made, on the specification and the experimentation of new metaphors for the visualization and interaction with the 4D digital twins through AR/VR devices. The building blocks will be integrated within a demonstrator and the overall approach will be validated through several proofs of concept (POCs) on scenarios related to the maintenance of manufactured products, the reconfiguration of production systems, and the quality control on production lines.

Although the progress in the efficiency of residential buildings, the consumption does not decrease as expected. Solution for involving inhabitants in sobriety and flexibility have already been proposed but they rely on site knowledge models. However each site is unique because of its architecture, equipment and sensors but also because of its occupants. The site models are mostly not available. LearningHome aims at developing cooperative and interactive learning for home inhabitants to confront to an Interactive Home Energy Management Aid System (IHEMAS) to yield knowledge about occupant activities and costs/comforts preferred compromise. LearningHome extends the promising concepts opened up by the ANR INVOLVED project regarding interactions by developing cooperative solutions to learn a global human-system learnt representation. The aim is to identify the practices as well as the activities of the occupants by reconciling the perceptions of the IHEMAS and its more or less numerous sensors with the perceptions of the inhabitants. These perceptions will be translated into activity labels, intentions and preferences, taking into account the volatility of the inhabitants' memory and limited consent to interact with an IHEMAS. It induces learning methods with ad hoc notifications but also mechanisms for matching the inhabitants and IHEMAS perceptions. It might be discrepancies between the IHEMAS and inhabitants perceptions because of a little number of sensors or because of a too high complexity in the inhabitant perceptions. These confusions must be resolved by automatically adapting for instance the generated features. Combining sensor data with labels from occupants yield a model thanks to learning algorithms. Interactive learning is a complementary method to discover the energy behavior of a site. Contrary to the INVOLVED approach, explanations and advice are generated without an a priori physical model, but by exploiting similar encountered situations. The aim is to conceive an exploratory approach guiding the inhabitants in the discovery of the effects of actions in similar situations. Inhabitants will thus be put in situation of experimenters of their environment and the IHEMAS will have the role of recording the experiments and guiding inhabitants towards new exploratory. It will engage inhabitants of residential buildings towards sober energy management through user interaction and that helps them to maintain their behavior change over time. According to J. Grudin, the future of Human Computer Interaction (HCI) are smart digital partners. Thus, the goal is to investigate mixed initiative through symmetrical co-learning interactions: both parties will inform, explain, ask, suggest and learn from the other. It is a new paradigm for IIHS, which fits well the unicity of each home where knowledge raises up from confrontation of parties. Our hypothesis is that co-learning will leverage user engagement as it puts users back in the decision loop by letting them to control the system boundaries. LearningHome will experiment different approaches to involve occupants to be more sober and more flexible in collective residential buildings. Different behavioral levers are going to be tested. One challenge is about measuring the impact of each lever: while it is relatively easy to measure a lever impact on energy consumption, it is difficult to assess an impact on energy used for heating. The assessments of the results follow two complementary approaches: an energy performance verification protocol for measuring over a few weeks the energy impacts of levers by measuring then for extrapolating them to a year by propagating uncertainties, and the analysis of household behavior regarding energy usage.

Collaboration 4.0 project is a contribution to the main industry of the future challenge of the efficient place of humans in the factory of the future. Industry of the future means the high-tech digitalization of production systems to get more flexibility of the whole value chain to achieve personalized products and sustainability. Enabling technologies like Internet Of Things, wearables, robotics, Artificial Intelligence and 3D Printings are the key drivers of this industrial transformation. The main challenge is to keep the economic value of mass production (3.0) when competitive lot-size 1 personalized production (4.0). The Collaboration 4.0 project aims at studying working situations enabled by the new digital technologies in 4.0 industrial environment for their productivity and attractive features. The project addresses the Nb 3 ANR research axis “Fostering industrial renewal” and especially the Nb 1sub-axis “Factory of the future: Human, organization and technologies”. Its overarching objective is to design collaborative workplaces of the future in which workers and machines are closely combined to reach new sustainable performance in 4.0 industrial environment. The project is featured from three fundamental research hypotheses: 1) The Human-Machine collaborative activity of the future will be carried out in a new enabling competence-based industrial environment, 2) Digital technologies are flexible and frequently evolve, 3) Work and industrial organization highly influences the well performing Human-Machine collaborative activity. The project aims at designing new workplaces in which workers and machines share the same space to complete shared tasks by using work-enabling digital technologies. The worker will manage work activities controlling the machine tasks and instructing it. The machine is designed to meet the worker needs. It could provide worker with new ways of working. We want to define and characterize the new types of 4.0 collaborative workplaces useful and well performing in a specific industrial situation. The core issues are the efficient technology uses while producing and the industrial organization to be set up. Concretely, the project will study two different work situations from two case studies: a collaborative activity between a robot and a human on one side and between an augmented reality wearable and a human on the other side. Delivered results will be a classification of human-Machine collaborative work situations in an enabling industrial environment, a framework for analyzing an enabling collaborative industrial activity and recommendations for designing enabling industrial workplaces. The project is a multidisciplinary project combining industrial engineering, ergonomics and digital technologies. It is featured in five scientific tasks and one management task. A workplace-of-the-future demonstrator will be developed at the Grenoble INP S.MART technological platform from existing facilities. An industrial advisory board accompany the research partners to operationalize the theoretical propositions. It is a 48-month project and relies on two PhD thesis and an engineer position. The project will be managed by G-SCOP laboratory (industrial engineering, augmented reality) alongside with LIG (robotics and HMI) and ACTé (ergonomics). Each laboratory will bring to the project their human resources and equipment as necessary. Project results will be spread through scientific publications, guidelines for industrial companies and communication activities.

Induced subgraphs play a central role in both structural and algorithmic graph theory. A graph H is an induced subgraph of a graph G if one can delete vertices of G to obtain H. This is the strongest notion of subgraph, hence being H-free (that is not containing H as an induced subgraph) is not a very restrictive requirement. Weaker notions of containment, like for instance minors, are now well understood, and the next achievement in Graph Theory should certainly be the understanding of forbidden induced structures. We focus in this proposal on the following very general question: Given a (possibly infinite) family F of graphs, what properties does a F-free graph have? This is the key question of many important and longstanding problems, because many crucial graph classes are defined in terms of forbidden induced subgraphs. This field is now quickly growing, and new techniques and tools have been recently developed. Our first goal is to establish bounds on some classical graph parameters for F-free graphs, such as the clique number, the stability number and the chromatic number. A second goal is to design efficient algorithms to recognize F-free graphs and to determine or approximate some parameters for those graphs. We also plan to study similar questions for oriented graphs. For this purpose, we plan to use and develop various proof techniques, some of these being recently discovered, such as the structural description of graph classes, the regularity lemma, graph limits, flag algebras, VC-dimension, discharging method as well as computer-assisted proofs.

The spread of ICT and cheap low-size production tools like 3D-printers led to the development of open design, i.e. community-based and open source development of physical products. This innovative organization of product development based on a new conception of copyright as well as decentralized and voluntary work offers a disruptive alternative to conventional industrial product development. It provides a great opportunity for continuous improvement of products as well as formidable potentials for product innovation and incubation of new businesses. However, the emergence of open design still suffers from a limited availability of supporting methods and online tools helping to face the organizational challenges raised by distributed collaboration of non-contractually engaged volunteers. Because of a lack of adapted structuration mechanisms, open design projects are still restricted to the development of products of low complexity and quality, i.e. prototypes or toys for do-it-yourself hobbyists. In order to compete with today’s standards of industrial product design, open design shall be provided with adapted methods and tools that ensures significant process efficiency and economic viability. The objective of this project is to develop an online Open Design Platform providing communities with innovative methods and tools allowing them to collaborate on the design and continuous improvement of open source products. The scope of the project covers 1) the characterization of the open design paradigm and the existing practices as they emerge today, 2) the development a set of adapted methods for the organization of open design projects based on the adaptation of existing methods used in industry 3) the prototyping of an online platform implementing these methods. Scientific challenges addressed along these tasks are to: - Define an open source product development process ensuring the convergence of open source product development projects and allowing designing high quality and complex products. - Define adapted supporting information systems providing design communities with tools for online collaboration, product data management and management of the development process. - Define guidelines for business models helping entrepreneurs reducing risks associated with the development of business models based on open source. The project will be performed by a multidisciplinary consortium involving French and German research laboratories and companies. It will be led by French and German laboratories on industrial engineering (G-SCOP [Fr], TU Berlin/IWF [De]) and will involve contributions from business economics (HU Berlin/HIIG [De]) and innovation management sciences (CERAG [Fr]). Solutions will be developed and tested in collaboration the company Raidlight [Fr] that develops an original co-design approch with its customers, and online communities thanks to our external partners (P2PLab, Open Source Ecology).