In sight of current low building renewal rates, the development of energy retrofitting activities for existing buildings emerges as a major challenge to reduce energy waste. External thermal insulation appears as an interesting solution. Trades such as wood and concrete construction must now turn to digital and collaborative planning tools to improve their efficiency and respond to this challenge. The ISOBIM project aims to contribute to the transformation of this sector by developing decision support tools integrated into a solution that fosters the digital engineering of retrofitting processes utilizing modular framing panels. The originality of the project lies in proposing an environment overarching optimizer models for nesting, along with a collaborative scheduling framework. The latter will work under constrained models of Lean logics and 4D simulation that will help validating overall results.

The Fourier phase retrieval problem appears in many fields of imaging, such as X-ray crystallography or coherent diffraction imaging (CDI). Being able to solve the phase retrieval problem is crucial to these imaging modalities. As a result, the topic of phase retrieval has given rise to numerous experimental, algorithmic and theoretical developments over the past several decades. Surprisingly, the consideration of the vectorial properties of light, i.e. polarization of light, in phase retrieval has only received limited interest. Recent experimental developments have shown its potential to decipher key properties of materials such as anisotropy, that are inaccessible to conventional non-polarized light. Considering polarization in phase retrieval leads to a new class of reconstruction problems called polarimetric phase retrieval (PPR). From a theoretical standpoint, the PPR problem admits an algebraic reformulation as the computation of the greatest common divisor (GCD) between polynomials constructed from experimental measurements. This equivalence is specific to PPR. It enables the use of algebraic reconstruction strategies based on approximate GCD computations relying on numerical linear algebra, leading to computationally efficient and accurate solutions. However, current approaches are limited to the 1D case, and exhibit poor performance in realistic signal-to-noise ratio scenarios. The ATEMPORAL project aims at filling the gap between existing 1D approaches and practical 2D object reconstruction from experimental measurements in polarimetric CDI.ATEMPORAL will introduce a new and complete algebraic and tensor framework for polarimetric phase retrieval by leveraging its intrinsic algebraic properties. To this end, it will introduce new 1D GCD-based algebraic approaches that are robust to realistic experimental noise levels and capable of handling prior information. Next, by exploiting the natural representation of polarized images as third-order tensors, a general methodology to lift 1D algebraic methods to the 2D case will be devised. This novel approach promise two crucial benefits. First, it will enable fast and accurate reconstructions to the polarimetric phase retrieval problem, allowing the complexity of the solution to be tailored to the experimental constraints. Second, these new algebraic and tensor methods will provide the high-quality cost-efficient initial points required to unlock optimal estimation performance of gradient descent-type algorithms, which are known to be highly sensitive to initial points and/or quite computationally demanding. The practical relevance of the proposed framework will be demonstrated by extensive benchmarking on state-of-the-art 2D polarimetric CDI experiments.

In 2009, the CRAN laboratory begun to study the concept of “communicating materials” where materials are able to communicate with their environment, process, exchange information, and store data in their own structure. Besides, they also have the capability to sense their environment and measure their own internal physical states. This concept has been applied to the construction industry and led a physical prototype based on RFID tags embedded into the product structure. However, RFID are limited in memory, and must be read at short distance. In another hand, BIM (Building Information Modelling) data and models are often limited to design phases and neither reused nor accessible for downstream actors. To solve both problems, the McBIM objectives are 1) to design a “communicating concrete”, made of concrete equipped with embedded low-energy wireless micro-sensor network, able to manage and exchange data with BIM platforms, and 2) to demonstrate the usefulness of this approach across two building lifecycle phases, namely the construction and exploitation phases (for structural health monitoring). To build this communicating concrete, several scientific obstacles should be solved: a) the design of robust wireless communications, not impacted by the concrete environment, b) the definition of innovative RF harvesting techniques to maximize the lifetime of embedded sensor nodes, c) the definition of new data management strategies controlling how data (either generated by sensor nodes, or sent by users) are spread into the WSN for a fast and reliable data storage and retrieval, d) the definition of a native BIM interoperability of the concrete material, based on IFC standard, to ensure a correct communication with BIM platforms. The McBIM consortium is composed of 4 partners (CRAN, LE2I, LAAS, 360SC), all gathering the needed competences for the project success. McBiM is running over 42 months and decomposed into 6 WPs, 2 dedicated to project management and specfications, 3 dedicated to previous enounced scientific obstacles, and 1 to prototype manufacturing and experimentation on sites. Due to its multi-disciplinary nature, the McBIM project is expected to have a large impact on multi-disciplinary science, combining the knowledge and techniques of the various disciplines to find new insights and new approaches, and create highly innovative solutions and products related to robust wireless transmission, low-energy micro-nodes adapted to reinforced concrete conditions, data management algorithms for WSN, interoperability with BIM platforms in the IoT. In agreement with the other partners, our partner 360SC, specialized in “connected concrete” could extend their offers to propose innovative data management and monitoring services to their clients, based on the project results.

The modernization of industrial systems is argued within the "Factory of the Future (FoF)" strategic plan as a necessary lever to improve competitiveness and maintain industrial employment in France. It raises major challenges of innovation that the PHM (Prognostics and Health Management) defended as a new scientific discipline initiated by NASA, is a major focus. PHM is structured on key processes such as monitoring / predictive diagnosis, prognostics and aided decision-making. These processes aim at characterizing the drifts and degradations of a system and prevent/anticipate its failures. While standards are created to formalize these processes, the PHM is not actually deployed in companies. In that way, ad hoc software solutions available are too limited, proprietary and not designed from a scientific framework enough consistent to ensure genericity. This scientific challenge is addressed with the research strategy of CRAN Laboratory (UMR CNRS 7039, University of Lorraine). The CRAN is recognized nationally and internationally as a major actor of PHM. It is initiator for founding many works on the diagnosis, prognosis, and predictive maintenance but also on their engineering in a system vision. These works are validated at laboratory level but not enough at the company one due to missing of suitable tools supporting the works transfer to an industrial scale. The PREDICT SME is a pioneer in the market of PHM tools in France because it is selling an offer built on CASIP / KASEM and CASIP Engineering platforms with the associated services required to configure these platforms. Nevertheless these solutions are mainly implemented within complex - unitary industrial systems whereas growth markets are moving towards application areas in which the systems have smaller size and complexity (e.g. machine tool, tractors, and construction equipment). It implies for the economic sustainability of PREDICT and the increasing of its overall leadership in PHM solutions, to adapt its offer with a reduction, in the total cost, by a factor of 10 to 100. This rationalization based on a modularization of PHM technologies is the key to open both the SME market / ETIs and the export market within a volume of sales adapted to these new markets. PREDICT must therefore innovate in three complementary scientific areas: models and algorithms to support PHM processes; the engineering to deploy these processes in the case of a specific system, and finally, the technologies to support the deployment. These technologies could be, in a short term, cyber-physical technologies built on a software part (PHM algorithms) that interacts with a hardware part such as Single-Board Computer, Plug computer or SmartPhone. This report on PHM about technology and scientific needs, added with the complementary skills between CRAN and PREDICT, are the genesis of this LabCOM proposal called PHM-FACTORY (or manufacture of cyber physical PHM technologies). It can be seen as an innovation and research platform for which the roadmap is based on 3 phases: consolidation (e.g. short-term transfer), expansion (e.g. new functionalities) and exploration (e.g. prospecting for new services). The roadmap will address issues such as knowledge operationalization, hybrid approaches, self-adaptive algorithms ... Expected on the 3 axes (models / engineering / technology), innovations will be realized by "software components (COTS)" and a specialist engineering enabling the development of innovative PHM solutions. The results will also benefit to the industrial and academic communities in strengthening the foundations of the PHM discipline and its effectiveness in business and operational context. Finally this LabCOM is a real opportunity to increase and sustain the already successful collaboration established between PREDICT and CRAN (from several years ago in terms of projects, co-authored publications …) in a stable institutional framework conducting to the emergence of R&D.

For about 30% of the patients suffering from focal epilepsy, pharmacological treatments appear to be inefficient and we need to resort to surgery. The procedure consists in first localizing the epileptogenic zone via a comprehensive examination which takes into account neurological examination, fMRI, EEG, SEEG or MEG. Afterwards, we proceed to the excision provided it will not have a major impact on the patient’s life. Surgery is efficient for only a quarter of drug-resistant patients. It is therefore essential to bring fresh insights into the seizures pathophysiology in order to open the way to novel localization paradigms which would increase the surgery success rate and potentially lead to novel treatments. In this basic research project, we want to develop and analyze patient-specific dynamical models of the epileptic network which reproduce realistic intracranial EEG activities for patients suffering from the most frequent intractable epilepsies: lobe temporal epilepsies. The model will be used to validate neurophysiological assumptions on the seizure-causing factors and might cast new lights on the localization problem of the (potential) seizure on-set zones. The originality of our approach relies on the use of control theory. We want to apply and develop novel methods from emerging control fields such as nonlinear estimation, networked systems synchronization, hybrid systems as well as innovative source localization and reconstruction techniques that will attract the attention of the control and signal processing communities respectively. This interdisciplinary project will be carried out in the CRAN (Nancy) and will involve control and signal processing researchers as well as neuroscientists and neurologists.