Artificial neural network for high-throughput spectral data processing in LIBS imaging: application to archaeological mortar

International audienceWith the development of micro-LIBS imaging, the ever-increasing size of datasets (sometimes >1 million spectra) makes the processing of spectral data difficult and time consuming. Advanced statistical methods have become necessary to process these data, but most of them still require strong expertise and are not adapted to fast data treatment or a high throughput analysis. To address these issues, we evaluate, in the present work, the use of an artificial neural network (ANN) for LIBS imaging spectral data processing for the identification of different mineral phases in archaeological lime mortar. Common in ancient architecture, this building material is a complex mixture of lime with one or more aggregates, some components of which are of the same chemical nature (e.g. calcium carbonates). In this study, we trained an artificial neural network (ANN) for automatic detection of different phases in these complex samples. The training of such a predictive model was made possible by building a LIBS dataset of more than 1300 reference spectra, obtained from various selected materials that may be present in mortars. The ANN parameters (pre-treatment of data, number of neurons and of iterations) were optimized to ensure the best recognition of mortar components, while avoiding overtraining. The results demonstrate a fast and accurate identification of each component. The use of an ANN appears to be a strong means to provide an efficient, fast and automated LIBS characterization of archaeological mortar, a concept that could later be generalized to other samples and other scientific fields and methods

A holistic contribution to fast innovation in electric vehicles: An overview of the DEMOBASE research project

This paper is a contribution to fasten integration of battery pack innovation in commercial Electric Vehicles (EV) through massive digitalization: a seamless process detailed for battery design, battery safety, and battery management. Selected results of studies carried out on the EV value chain from design to recycling steps are presented, highlighting the importance of seamless integration and holistic state of mind when designing EV. Association between experimental and numerical approaches for efficient innovative EV production is crucial to achieve easy commercialisation. Successful forecasting of aging and thermal runaway evolution from single cell failure at module level using such methods illustrates their great potential. Hardware key counterparts under development are also introduced and give an idea of future architecture of EV battery packs and overall improvement of EV energy efficiency. Finally, a flexible and easily modifiable solution for battery electric vehicle (BEV) that allows rapid and cost-effective integration of future innovation is presented. This paper globally illustrates key breakthroughs gained in the context of the collaborative research project named ‘DEMOBASE’, for DEsign and MOdelling for improved BAttery Safety and Efficiency successfully submitted for funding by the European Commission in response to a 2017 call dedicated to ‘Green Vehicles’ under the EU Horizon 2020 work programme “Smart, green and integrated transport”

A holistic contribution to fast innovation in electric vehicles: An overview of the DEMOBASE research project

This paper is a contribution to fasten integration of battery pack innovation in commercial Electric Vehicles (EV) through massive digitalization: a seamless process detailed for battery design, battery safety, and battery management. Selected results of studies carried out on the EV value chain from design to recycling steps are presented, highlighting the importance of seamless integration and holistic state of mind when designing EV. Association between experimental and numerical approaches for efficient innovative EV production is crucial to achieve easy commercialisation. Successful forecasting of aging and thermal runaway evolution from single cell failure at module level using such methods illustrates their great potential. Hardware key counterparts under development are also introduced and give an idea of future architecture of EV battery packs and overall improvement of EV energy efficiency. Finally, a flexible and easily modifiable solution for battery electric vehicle (BEV) that allows rapid and cost-effective integration of future innovation is presented. This paper globally illustrates key breakthroughs gained in the context of the collaborative research project named ‘DEMOBASE’, for DEsign and MOdelling for improved BAttery Safety and Efficiency successfully submitted for funding by the European Commission in response to a 2017 call dedicated to ‘Green Vehicles’ under the EU Horizon 2020 work programme “Smart, green and integrated transport”