Application of a filtering method for the estimation of effective transmission condition parameters from ultrasonic data
In a recently completed thesis work, a filtering strategy combining both iterations of a Levemberg-Marquardt descent method with a gradient-free Kalman filtering approach has been developed. First evaluations of the algorithm have been carried out in order to reconstruct the pre-deformation of a plate geometry from guided wave ultrasound data. In this context, the main objective of the proposed work is on the one hand to consolidate the knowledge and implementation of the proposed approach, and to confirm its efficiency and interest in other ultrasonic NDT configurations. A particular application case of interest in the framework of this work will be the reconstruction of the Effective Transmission Conditions (ETCs) parameters that can typically represent: a delamination defect between two layers of a composite material, an imperfect bonding between an ultrasonic sensor and the inspected part, or an interface presenting a roughness of characteristic dimensions lower than the minimum wavelength used for the control. In practical industrial cases, the parameters of these ETCs are difficult to obtain. Thus, the interest of setting up a filtering process is to offer, in complex cases, an automatic calibration of the effective parameters of these models.
Digital correction of the health status of an electrical network
Cable faults are generally detected when communication is interrupted, resulting in significant repair costs and downtime. Additionally, data integrity becomes a major concern due to the increased threats of attacks and intrusions on electrical networks, which can disrupt communication. Being able to distinguish between disruptions caused by the degradation of the physical layer of an electrical network and an ongoing attack on the energy network will help guide decision-making regarding corrective operations, particularly network reconfiguration and predictive maintenance, to ensure network resilience. This study proposes to investigate the relationship between incipient faults in cables and their impact on data integrity in the context of Power Line Communication (PLC). The work will be based on deploying instrumentation using electrical reflectometry, combining distributed sensors and AI algorithms for online diagnosis of incipient faults in electrical networks. In the presence of certain faults, advanced AI methods will be applied to correct the state of the health of the electrical network's physical layer, thereby ensuring its reliability.
Characterisation of fast transient phenomena using X-ray phase contrast imaging
The aim of this post-doctorate is to develop a measurement chain dedicated to the observation and characterisation of Rapid Transient Phenomena (RTP) using X-ray phase contrast imaging (XPCI). The challenge is to provide a measurement system that can be deployed in the laboratory on a wide range of experiments that cannot be moved to the synchrotron. The performance targets are justified by the problems associated with additive manufacturing, the propagation of shock waves in low-density polymers, and the diagnosis of carbon composite materials impacted by an electric arc.
For objects with low absorption, such as low-density polymers, liquids or plasmas, conventional X-ray imaging, which provides contrast due to the absorption cross-sections variations, is insufficient. To complete absorption, it is possible to exploit the phase of X-rays, which provides better detection of inhomogeneities and interfaces. The method used here to measure the phase is the multilateral shearing interferometry (IDML). It uses a single two-dimensional checkerboard phase grating that generates a reference interference pattern on the detector. The introduction ofan object between the grating and the detector modifies the reference interference pattern, which is then analysed by Fourier transform to reconstruct the phase image. By requiring only a single phase grid and exhibiting minimal X-ray flux loss, this method has favourable intrinsic characteristics in terms of sensitivity, robustness, ease of alignment and versatility, for application to dynamic imaging.
Development of a new atomic reference database for radioactive processes
Several scientific communities have highlighted the lack of precision and the inconsistencies present in the reference atomic database EADL. The data were calculated using a fairly simple Dirac-Hartree-Slater approach and then subsequently corrected empirically. However, to date it remains the only database that is sufficiently complete to be usable by simulation codes. In recent years, a collaboration was initiated and reinforced during two successive European projects between the FCT-UNL (Lisbon, Portugal), the IPCMS (Strasbourg, France) and the LNHB (CEA Saclay, France). A new relativistic atomic code, based on the density functional theory, has been developed and validated by studying different electron capture transition probabilities. The aim of the present subject is to develop a new reference atomic database based on this atomic code. The required theoretical equations will have to be established. Several elements will be calculated and the predictions will be compared to available results in the literature. The influence of this precise atomic modeling on the atomic exchange effect that occurs in beta transitions will also be studied. At least one publication and one participation to an international conference are expected.
As part of a project that concerns the creation of innovative materials, we wish to strengthen our platform in its ability to learn from little experimental data.
In particular, we wish to work firstly on the extraction of causal links between manufacturing parameters and properties. Causality extraction is a subject of great importance in AI today and we wish to adapt existing approaches to experimental data and their particularities in order to select the variables of interest. Secondly, we will focus on these causal links and their characterization (causal inference) using an approach based on fuzzy rules, that is to say we will create fuzzy rules adapted to their representation.
Calibration of the high dose rate flash therapy beam monitor of the IRAMIS facility
Ultra-flash beams are pulsed beams of high-energy electrons (over a hundred MeV) with pulse durations in the femto-second range. The IRAMIS facility (CEA Saclay) uses laser acceleration to produce this type of beam, with a view to their application in radiotherapy. The LNHB is in charge of establishing dosimetric traceability for the IRAMIS facility, and to do this it has to calibrate the facility's monitor. Current radiotherapy facilities are based on medical linear accelerators operating at energies of up to 18 MeV in electron mode. LNHB has such equipment. It is used to establish national references in terms of absorbed dose to water, under the conditions of the IAEA protocol TRS 398.
Establishing dosimetric traceability involves choosing the measurement conditions, knowing the transfer dosimeter characteristics used and any corrections to be applied to the measurements taking into account the differences between the IRAMIS Facility and those of LNHB.
Optimization of a metrological approach to radionuclide identification based on spectral unmixing
The Laboratoire national Henri Becquerel (LNE-LNHB) at CEA/Saclay is the laboratory responsible for French references in the field of ionizing radiations. For several years now, it has been involved in the development of an automatic analysis tool for low-statistics gamma spectra, based on the spectral unmixing technique. This approach makes it possible to respond to metrological constraints such as robust decision-making and unbiased estimation of counts associated with identified radionuclides. To extend this technique to field measurements, and in particular to the deformation of spectra due to interactions in the environment of a radioactive source, a hybrid spectral unmixing model combining statistical and automatic learning methods is currently being developed. The aim of this mathematical solution is to implement a joint estimation of the spectra measured and the counts associated with the radionuclides identified. The next step will be to quantify the uncertainties of the quantities estimated from the hybrid model. The aim is also to investigate the technique of spectral unmixing in the case of neutron detection with a NaIL detector. The future candidate will contribute to these various studies in collaboration with the Laboratoire d'ingénierie logicielle pour les applications scientifiques (CEA/DRF).
Development of Algorithms for the Detection and Quantification of Biomarkers from Voltammograms
The objective of the post-doctoral research is to develop a high-performance algorithmic and software solution for the detection and quantification of biomarkers of interest from voltammograms. These voltammograms are one-dimensional signals obtained from innovative electrochemical sensors. The study will be carried out in close collaboration with another laboratory at CEA-LIST, the LIST/DIN/SIMRI/LCIM, which will provide dedicated and innovative electrochemical sensors, as well as with the start-up USENSE, which is developing a medical device for measuring multiple biomarkers in urine.
POST-DOC/CDD X-ray tomography reconstruction based on Deep-Learning methods
CEA-LIST is developing the CIVA software platform, a benchmark for the simulation of non-destructive testing processes. In particular, it offers tools for X-ray and tomographic inspection which, for a given inspection, can simulate all radiographies, taking into account various associated physical phenomena, as well as the corresponding tomographic reconstruction. CEA-LIST also has an experimental platform for robotized X-ray tomography inspection.
The proposed work is part of the laboratory's contribution to a bilateral French-German ANR project involving academic and industrial partners, focusing on the inspection of large-scale objects using the robotized platform. A sufficient number of X-rays must be taken in order to carry out a 3D reconstruction of the object. In many situations, some angles of view cannot be acquired due to the dimensions of the object and/or the motion limitations of the robots used, resulting in a loss of quality in the 3D reconstruction.
Expected contributions focus on the use of Deep-Learning methods, to complete missing projections on the one hand, and reduce reconstruction artifacts on the other. This work includes the CIVA-based steps of building a simulated database and evaluating the obtained results using POD (Probability Of Detection) measurements.
The candidate will have access to the facilities of the Paris Saclay research center and will be expected to promote his/her results in the form of scientific communications (international conferences, publications).
Candidate profile:
PhD in data processing or artificial intelligence.
Fluent English (oral presentations, scientific publications).
Previous knowledge of X-ray physics and tomographic reconstruction methods would be appreciated.
X-ray tomography reconstruction based on analytical methods and Deep-Learning
CEA-LIST develops the CIVA software platform, a reference for the simulation of non-destructive testing processes. In particular, it proposes tools for X-ray and tomographic inspection, which allow, for a given tomographic testing, to simulate all the radiographic projections (or sinogram) taking into account various associated physical phenomena, as well as the corresponding tomographic reconstruction.
The proposed work is part of the laboratory's contribution to a European project on tomographic testing of freight containers with inspection systems using high-energy sources. The spatial constraints of the projection acquisition stage (the trucks carrying the containers pass through an inspection gantry) imply an adaptation of the geometry of the source/detector system and consequently of the corresponding reconstruction algorithm. Moreover, the system can only generate a reduced number of projections, which makes the problem ill-posed in the context of inversion.
The expected contributions concern two distinct aspects of the reconstruction methodology from the acquired data. On the one hand, it is a question of adapting the analytical reconstruction methods to the specific acquisition geometry of this project, and on the other hand, to work on methods allowing to overcome the lack of information related to the limited number of radiographic projections. In this objective, supervised learning methods, more specifically by Deep-Learning, will be used both to complete the sinogram, and to reduce the reconstruction artifacts caused by the small number of projections available. A constraint of adequacy to the data and the acquisition system will also be introduced in order to generate physically coherent projections.