IMT Atlantique
  • Nantes, Brittany, France
Recent publications
Identification from field measurements allows several parameters to be identified from a single test, provided that the measurements are sensitive enough to the parameters to be identified. To do this, authors use empirically defined geometries (with holes, notches...). The first attempts to optimize the specimen to maximize the sensitivity of the measurement are linked to a design space that is either very small (parametric optimization), which does not allow the exploration of very different designs, or, conversely, very large (topology optimization), which sometimes leads to designs that are not regular and cannot be manufactured. In this paper, an intermediate approach based on a non-invasive CAD-inspired optimization strategy is proposed. It relies on the definition of univariate spline Free-Form Deformation boxes to reduce the design space and thus regularize the problem. Then, from the modeling point of view, a new objective function is proposed that takes into account the experimental setup and constraint functions are added to ensure that the gain is real and the shape physically sound. Several examples show that with this method and at low cost, one can significantly improve the identification of constitutive parameters without changing the experimental setup.
It remains challenging to produce new and efficient adsorbents to remove sulfur compounds from waste tire pyrolysis oils. The present work investigated the ex-situ treatment by desulfurization and cracking of volatiles from the pyrolysis of waste tires using an innovative synthetic oil mixture. For this purpose, biochars from the pyrolysis of abundant biomasses (date seeds and spent coffee grounds) were used to improve the quality of these volatiles. Furthermore, the purification efficiency of these biochars was compared to that of commercial activated carbon. Thus, the influence of the operating conditions was studied. Finally, to provide an innovative insight into the desulfurization efficiency, the regeneration of the best performing desulfurization material was carried out. The results show promising desulfurization and cracking capacities of the spent coffee grounds biochar and excellent regeneration performance.
The critical region of unmoderated molten salt reactors consists in a cavity filled with a liquid fuel. The lack of internal structure implies a complex flow structure of the circulating fuel salt. A preliminary core shape optimization has been performed during the EVOL European project to limit recirculation and hotspots. This optimization was based on a Reynolds Averaged Navier Stokes (RANS) approach, but the latter only provides time-averaged values for velocity and temperature. However, the power stability is sensitive to thermal fluctuations induced by the flow turbulence itself, even at steady state without pump flow rate or heat extraction variation. This phenomenon is studied using a Detached Eddies Simulation approach to solve the turbulence in the reactor and get a time dependent temperature distribution and then the reactivity fluctuations. A new geometry is proposed to limit the total power fluctuations from 7.5% for the preconceptual EVOL geometry down to 1.2%.
Studies examining the potential of augmented reality (AR) to improve assembly tasks are often unrepresentative of real assembly line conditions and assess mental workload only through subjective measurements and leads to conflicting results. We proposed a study directly carried out in industrial settings, to compare the impact of AR-based instructions to computerized instructions, on assembly effectiveness (completion time and errors) and mental workload using objective (eye tracking), subjective (NASA-TLX) and behavioral measurements (dual task paradigm). According to our results, AR did not improve effectiveness (increased assembly times and no decrease in assembly errors). Two out of three measurements indicated that AR led to more mental workload for simple assembly workstation, but equated computer instructions for complex workstation. Our data also suggest that, AR users were less able to detect external events (danger, alert), which may play an important role in the occurrence of work accidents.
The Lacaze-Duthiers Canyon is located in the western Mediterranean Sea and is long known for hosting cold-water coral colonies in the canyon head region at depths ranging from 250 to 550 m. In 2019 during the CALADU cruise, three kinds of 3D-reconstructions were applied to better understand the distribution of coral colonies, their habitat and their skeleton morphologies. The canyon's flanks were mapped using a hull-mounted echosounder and an ROV multibeam echosounder. Digital terrain models were built with resolutions of 5 and 1 m and examined in three dimensions. ROV bathymetric data collected on the canyon's flanks made it possible to highlight a series of sub-parallel structures identified as lithified sedimentary strata along which coral colonies grow. Coral assemblages were explored at four locations and photographic images were assembled using structure from motion techniques to build photogrammetric models. Coral assemblages reconstructed in 3D enabled geo-localizing and recreating coral colonies on 16 models over a total area of 4370 m². Two colonial species, Madrepora oculata and Desmophyllum pertusum were plotted and reported on bathymetric models to interpret their location at the scale of the canyon. The coordinates and depth of the colonies were used to calculate the vertical distribution (limited to our small bathymetric exploration, between 339 and 214 m depth) and density of populations (up to 4.3 colonies per m²). The spatial coverage of the 16 assemblages measured between 100 and 600 m² each. The sizes of the colonies were measured to analyze the population structures of both species (mean sizes of 28 cm for D. pertusum and 18 cm for M. oculata, maximum sizes 1 m and 0.5 m, respectively, bushes 2.5 m long). In addition, lost fishing gears were quantified, longlines measured and their densities calculated (0.16 m/m², up to 0.30 m/m²). An area with exuberant orange colonies of D. pertusum was discovered for the first time in the Lacaze-Duthiers Canyon. Five deep-sea scleractinian species were collected and micro-tomographic scans computed to view their intrinsic skeleton organization. Micro-CT scans of M. oculata, D. pertusum, Desmophyllum dianthus, Caryophyllia smithii, and Dendrophyllia cornigera enabled longitudinal and transversal cuts, highlighting morphological criteria for species identification and the multidirectional examination of specimens. We observed a thin canal connecting calices along the axis of D. pertusum colonies, and separate calices along the axis of M. oculata colonies.
A rich ecosystem of blockchain-based projects has emerged since the introduction of Bitcoin in 2008. New protocols seek to improve the performances of blockchain systems. In particular, the energy consumption of blockchains has been particularly decried. Unfortunately, those new proposals are often evaluated with ad hoc tools and experimental environments. Therefore, reproducibility and comparison of these new contributions with the state of the art of blockchain technologies are complicated. To the best of our knowledge, only a few tools partially address the design of a generic benchmarking of blockchain technologies (e.g., load generation). This paper introduces BCTMark, a generic framework for benchmarking blockchain technologies on an emulated network in a reproducible way. Based on this novel framework, we studied a key aspect of modern blockchains’ energy consumption: smart-contract execution. Based on experiments and the analysis of one year of real-world Ethereum transactions, we measured and modeled smart-contracts’ energy consumption on Ethereum. Furthermore, this study details how the replication of contract calls execution can impact their energy cost. In particular, we give insights on the energy consumed by smart-contracts on Ethereum over one year.
By using Takagi–Sugeno (T–S) fuzzy set approach, this paper proposes a robust dynamic output feedback (DOF) control for nonlinear fractional-order systems satisfying \(0<\alpha <1\). First, using a Fractional Lyapunov function, the novel DOF controller guarantees the stability of the closed-loop system. The proposed approach allows avoiding appearance of crossing terms between the controller’s and the T–S system’s input matrices leading to easier LMI formulation. Second, a new controller is developed by combining a fuzzy dependent Lyapunov function and some special derivations on the controller parameters. This leads to some sufficient conditions in the form of strict linear matrix inequalities (LMIs). When compared with previous work, the proposed method not only has abilities to handle the fuzzy system with the time-derivatives of the membership functions but also can deal with the parametric uncertainties effectively. Simulation examples are provided to demonstrate the validity of the proposed conditions.
In this paper, we propose a new collaborative process that aims to detect macrocalcifications from mammographic images while minimizing false negative detections. This process is made up of three main phases: suspicious area detection, candidate object identification, and collaborative classification. The main concept is to operate on the entire image divided into homogenous regions called superpixels which are used to identify both suspicious areas and candidate objects. The collaborative classification phase consists in making the initial results of different microcalcification detectors collaborate in order to produce a new common decision and reduce their initial disagreements. The detectors share the information about their detected objects and associated labels in order to refine their initial decisions based on those of the other collaborators. This refinement consists of iteratively updating the candidate object labels of each detector following local and contextual analyses based on prior knowledge about the links between super pixels and macrocalcifications. This process iteratively reduces the disagreement between different detectors and estimates local reliability terms for each super pixel. The final result is obtained by a conjunctive combination of the new detector decisions reached by the collaborative process. The proposed approach is evaluated on the publicly available INBreast dataset. Experimental results show the benefits gained in terms of improving microcalcification detection performances compared to existing detectors as well as ordinary fusion operators.
Clinical diagnosis of the pediatric musculoskeletal system relies on the analysis of medical imaging examinations. In the medical image processing pipeline, semantic segmentation using deep learning algorithms enables an automatic generation of patient-specific three-dimensional anatomical models which are crucial for morphological evaluation. However, the scarcity of pediatric imaging resources may result in reduced accuracy and generalization performance of individual deep segmentation models. In this study, we propose to design a novel multi-task, multi-domain learning framework in which a single segmentation network is optimized over the union of multiple datasets arising from distinct parts of the anatomy. Unlike previous approaches, we simultaneously consider multiple intensity domains and segmentation tasks to overcome the inherent scarcity of pediatric data while leveraging shared features between imaging datasets. To further improve generalization capabilities, we employ a transfer learning scheme from natural image classification, along with a multi-scale contrastive regularization aimed at promoting domain-specific clusters in the shared representations, and multi-joint anatomical priors to enforce anatomically consistent predictions. We evaluate our contributions for performing bone segmentation using three scarce and pediatric imaging datasets of the ankle, knee, and shoulder joints. Our results demonstrate that the proposed approach outperforms individual, transfer, and shared segmentation schemes in Dice metric with statistically sufficient margins. The proposed model brings new perspectives towards intelligent use of imaging resources and better management of pediatric musculoskeletal disorders.
During the learning process, a child develops a mental representation of the task he or she is learning. A Machine Learning algorithm develops also a latent representation of the task it learns. We investigate the development of the knowledge construction of an artificial agent through the analysis of its behavior, i.e., its sequences of moves while learning to perform the Tower of Hanoï(TOH) task. The TOH is a well-known task in experimental contexts to study the problem-solving processes and one of the fundamental processes of children’s knowledge construction about their world. We position ourselves in the field of explainable reinforcement learning for developmental robotics, at the crossroads of cognitive modeling and explainable AI. Our main contribution proposes a 3-step methodology named Implicit Knowledge Extraction with eXplainable Artificial Intelligence (IKE-XAI) to extract the implicit knowledge, in form of an automaton, encoded by an artificial agent during its learning. We showcase this technique to solve and explain the TOH task when researchers have only access to moves that represent observational behavior as in human-machine interaction. Therefore, to extract the agent acquired knowledge at different stages of its training, our approach combines: first, a Q-learning agent that learns to perform the TOH task; second, a trained recurrent neural network that encodes an implicit representation of the TOH task; and third, an XAI process using a post-hoc implicit rule extraction algorithm to extract finite state automata. We propose using graph representations as visual and explicit explanations of the behavior of the Q-learning agent. Our experiments show that the IKE-XAI approach helps understanding the development of the Q-learning agent behavior by providing a global explanation of its knowledge evolution during learning. IKE-XAI also allows researchers to identify the agent’s Aha! moment by determining from what moment the knowledge representation stabilizes and the agent no longer learns.
In this paper, we study a paratransit application in which children are transported every day from their homes to their special schools or medical-social establishments. To optimize this transportation system, the establishments collaborate to propose a joint transportation plan. We propose a new algorithm to jointly build vehicle routes that visit several establishments and simultaneously set the establishments’ opening hours. This algorithm combines a large neighborhood search, the resolution of a route-based model, and the progressive shrinkage of the planning window. It is applied to a real case from the area of Lyon in France, including 34 schools and 575 heterogeneous users served by a heterogeneous fleet of reconfigurable vehicles. On average, we show that in addition to the 10% of saving that can be expected by sharing vehicle routes between schools, 7% of additional savings can be achieved by school bell adjustment. This cost saving also decreases average user ride times and the number of vehicles required, creating longer routes that are more attractive for driver services.
Human activity recognition (HAR) is fundamental to many services in smart buildings. However, providing sufficiently robust activity recognition systems that could be confidently deployed in an ordinary real environment remains a major challenge. Much of the research done in this area has mainly focused on recognition through pre-segmented sensor data. In this paper, real-time human activity recognition based on streaming sensors is investigated. The proposed methodology incorporates dynamic event windowing based on spatio-temporal correlation and the knowledge of activity trigger sensor to recognize activities and record new events. The objective is to determine whether the last event that just happened belongs to the current activity, or if it is the sign of the start of a new activity. For this, we consider the correlation between sensors in view of what can be seen in the history of past events. The proposed algorithm contains three steps: verification of sensor correlation (SC), verification of temporal correlation (TC), and determination of the activity triggering the sensor. The proposed approach is applied to a real case study: the “Aruba” dataset from the CASAS database. F1 score is used to assess the quality of the segmentation. The results show that the proposed approach segments several activities (sleeping, bed to toilet, meal preparation, eating, housekeeping, working, entering home, and leaving home) with an F1 score of 0.63–0.99.
Constraint Programming is a powerful paradigm to model and solve combinatorial problems. While there are many kinds of constraints, the table constraint is perhaps the most significant—being the most well-studied and has the ability to encode any other constraints defined on finite variables. However, constraints can be very voluminous and their size can grow exponentially with their arity. To reduce space and the time complexity, researchers have focused on various forms of compression. In this paper, we propose a new approach based on maximal frequent itemsets technique and area measure for enumerating the maximal frequent itemsets relevant for compressing table constraints. Our experimental results show the effectiveness and efficiency of this approach on compression and on solving compressed constraint satisfaction problem.
We present a new eye-tracking and target designation device based on a contact lens incorporating a pair of vertical-cavity surface-emitting lasers (VCSELs). We describe the operating principle, the manufacturing process and characterize the impact of the VCSELs encapsulation on their optical properties. We then describe how such device can be incorporated into an eye-wear or a visual augmented system. We compare two different detection set-ups, the first using a camera and the second a position sensitive device, both illustrating different laser beam detection modes. We analyze their performances in terms of angular accuracy, speed, compactness, manufacturability, compared to current conventional eye-tracking systems. We emphasize how the use of two VCSELs and the control of their orientation during the encapsulation can simplify their integration in host systems and improve the gaze detection performance. Finally, we describe various embodiments and discuss potential improvements that can be expected in future systems.
Supply chain finance (SCF) provides credit for small and medium-sized enterprises with low credit lines and small financing scales. The resulting financial credit data and related business transaction data are highly confidential and private. However, traditional SCF management schemes use third-party platforms and centralized designs that cannot achieve highly reliable secure storage and fine-grained access control. To address such a need, we propose Fabric-SCF, designing and implementing a Blockchain-based secure storage system by utilizing distributed consensus to realize data security, traceability, and immutability. The attribute-based access control model is deployed for access control, also utilizing smart contracts to define system processes and access policies to ensure the system’s efficient operation. To verify the performance of Fabric-SCF, two sets of simulation experiments are designed its effectiveness. Experimental results show that Fabric-SCF achieves dynamic and fine-grained access control while maintaining high throughput in a simulated real-world operating scenario.
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859 members
Alexandre Khaldi
  • Department of optics
Yann Busnel
  • Network Systems, Cybersecurity and Digital Law
Technopôle Brest Hirwazh, 29200 Brest, Nantes, Brittany, France