Optimal active sensing control for two-frame systems

International audienceThis paper provides a complete characterization of the trajectories that maximize the information collected by a moving vehicle, through sensors' measurements, for the recently introduced class of nonlinear "two-frame systems". The information is quantified in terms of the trace of the observability Gramian (OG) along a trajectory. In general, this quantity nontrivially depends on the control inputs and the state trajectory, resulting in a difficult optimal control problem. Herein, we leverage the property of invariant filtering that Jacobians are state-trajectory independent, that is, only depend on the control inputs, which enables us to mathematically derive optimal trajectories in closed form. We illustrate the results numerically on problems from robotics such as 3D robot localization, and 2D simultaneous localization and mapping

Analysis of the effect of surface mechanical attrition treatment on the mechanical properties of 17-4 PH stainless steel obtained by material extrusion

International audienceIn this study, the influence of the surface mechanical attrition treatment (SMAT) on a 17-4PH stainless steel made by material extrusion additive manufacturing is investigated under mechanical loading. The evolutions of the deformations at the local scale have been performed during in-situ tensile tests up to failure around 4kN. The strain maps are obtained with an original process based on the use of nanogauges displacement from the recorded of scanning electron microscope images. These maps allow to analyze the deformation mechanisms of as-fabricated and mechanically treated samples. The porosities evolutions at the surface are especially investigated for the two types of samples. The crack propagation in the as-fabricated samples is strongly influenced by porosities/defects related to the additive manufacturing process. Moreover, the SMATed samples present slip bands at the surface during the deformation. This deformation mechanism is similar to the one commonly observed in metallic materials obtained with traditional processes. Even if the application of SMAT does not show huge modifications of tensile properties with similar ultimate tensile strength around 650 MPa, it allows to improve the material surface quality by drastically reducing the surface roughness. SMAT treatment also allows a reduction of porosities in few microns from the sample surface. These improvements present a limited impact on tensile properties, but leads to its possible use for industrial applications when applied as an innovative post-treatment on metal part obtained by additive manufacturing

Estimation de la qualité d'une segmentation par apprentissage profonde basée sur l'entropie de la prédiction

International audienceImage segmentation is a common intermediate operation in many image processing applications. On automated systems it is important to evaluate how well it, or its subsystems are performing without access to the Ground Truth. In Deep Learning based image segmentation there are very few methods to evaluate the output quality without using a ground truth. Most of them are based on the uncertainty (variance or standard deviation) of the prediction and can be applied to Bayesian Neural Networks, but not to Convolutional Neural Networks. In this research we propose to use the Entropy as a measure of uncertainty applied to the segmented image predicted by the Neural Network and some indicators based on it. The method is tested in a segmentation task of labeled skin images. The entropy based indicators are evaluated without knowing the ground truth and compared with indicators based on the real labels (Jaccard, Dice and Average Symmetrical Surface Distance). This experimentation showed that they are correlated and some Entropy based indicators can predict quite well the ground truth based indicators.La segmentation d'images est une opération intermédiaire courante dans de nombreuses applications de traitement d'images. Sur les systèmes automatisés, il est important d'évaluer leurs performances, ou celles de leurs sous-systèmes, sans accès à la vérité terrain. Dans la segmentation d'images basée sur le Deep Learning, il existe très peu de méthodes pour évaluer la qualité du résultat sans utiliser une vérité terrain. La plupart d'entre elles sont basées sur l'incertitude (variance ou écart type) de la prédiction et peuvent être appliqués aux réseaux de neurones bayésiens, mais pas aux réseaux de neurones convolutifs. Dans cette recherche, nous proposons d'utiliser l'Entropie comme mesure d'incertitude appliquée à l'image segmentée prédite par le Réseau Neuronal et à certains indicateurs basés sur celle-ci. La méthode est testée dans une tâche de segmentation d’images cutanées étiquetées. Les indicateurs basés sur l'entropie sont évalués sans connaître la vérité terrain et comparés à des indicateurs basés sur les étiquettes réelles (Jaccard, Dice et Average Symmetrical Surface Distance). Cette expérimentation a montré qu'ils sont corrélés et que certains indicateurs basés sur l'entropie peuvent assez bien prédire les indicateurs basés sur la vérité terrain

Improving cross-site generalisability of vision-based solar forecasting models with physics-informed transfer learning

Forecasting solar energy from cloud cover observations is crucial to truly anticipate future changes in power supply. On an intra-hour timescale, ground-level sky cameras located near a solar site offer the most valuable source of information on incoming clouds. In the literature, the analysis of these hyperlocal cloud cover observations for solar modelling is increasingly performed by deep learning algorithms trained and tested on years’ worth of local data. However, this approach is not suitable for industrial applications since solar energy producers cannot wait for years of local data collection to start generating reliable solar forecasts. However, they might own relevant multi-location data collected from other solar sites over time. This study thus explores the capability of such algorithms to generalise beyond their training location in two data scarce conditions: zero-shot learning (i.e. direct application of a trained model to a new location without local fine-tuning) and few-shot learning (i.e. calibration of a pre-trained model based on very limited local data such as a day of observations). Zero-shot learning results show that using local clear-sky models to normalise output variables (e.g. solar irradiance or solar energy production values) facilitates cross-dataset transfer learning. Compared to previous methods, the resulting forecast skill increases by close to 25% in cloudy conditions and by more than 700% in clear-sky conditions. An additional gain is observed when local data collected in overcast weather conditions are used for model calibration via few-shot learning. The corresponding neural networks trained in data scarce conditions achieve comparable performance to expert local models based on years of training data. These promising results shed light on the potential of large-scale and multi-location sky image datasets to improve the generalisation skills of solar forecasting algorithms

Mean-Square Exponential Stabilization of Mixed-Autonomy Traffic PDE System

International audienceControl of mixed-autonomy traffic where Human-driven Vehicles (HVs) and Autonomous Vehicles (AVs) coexist on the road has gained increasing attention over the recent decades. This paper addresses the boundary stabilization problem for mixed traffic on freeways. The traffic dynamics are described by uncertain coupled hyperbolic partial differential equations (PDEs) with Markov jumping parameters, which aim to address the distinctive driving strategies between AVs and HVs. Considering that the spacing policies of AVs vary in mixed traffic, the stochastic impact area of AVs is governed by a continuous Markov chain. The interactions between HVs and AVs such as overtaking or lane changing are mainly induced by impact areas. Using backstepping design, we develop a full-state feedback boundary control law to stabilize the deterministic system (nominal system). Applying Lyapunov analysis, we demonstrate that the nominal backstepping control law is able to stabilize the traffic system with Markov jumping parameters, provided the nominal parameters are sufficiently close to the stochastic ones on average. The mean-square exponential stability conditions are derived, and the results are validated by numerical simulations

Group Equivariant Networks Using Morphological Operators

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Two-tone spectroscopy of high-frequency quantum circuits with a Josephson emitter

International audienceWe perform two-tone spectroscopy on quantum circuits, where high-frequency radiation is generated by a voltage-biased superconductor-normal-superconductor Josephson junction and detection is carried out by an ancillary microwave resonator. We implement this protocol on two different systems, a transmon qubit and a $\lambda/4$ resonator. We demonstrate that this two-tone Josephson spectroscopy operates well into the millimeter-wave band, reaching frequencies larger than 80 GHz, and is well-suited for probing highly coherent quantum systems

Synthetic Dataset of Maneuvering Low Earth Orbit Satellite Trajectories for AI Analysis

International audienceThe characterization of satellite behavior is of paramount importance in Space Surveillance Awareness. It involves modeling complex patterns from large operational databases, making AI tools well-suited to handle this use case. Despite existing contributions, no database is dedicated to Pattern-of-Life study in the Low Earth Orbit regime. In this context, we provide a dataset of satellite trajectories, focusing on station-keeping issues. The proposed database contains generated trajectories based on real data. Our experiments on the provided dataset and real trajectories tend to verify the representativity of the data and highlight the complexity of the Pattern-of-Life related tasks.The characterization of satellite behavior is of paramount importance in Space Surveillance Awareness. It involves modeling complex patterns from large operational databases, making AI tools well-suited to handle this use case. Despite existing contributions, no database is dedicated to Pattern-of-Life study in the Low Earth Orbit regime. In this context, we provide a dataset of satellite trajectories, focusing on station-keeping issues. The proposed database contains generated trajectories based on real data. Our experiments on the provided dataset and real trajectories tend to verify the representativity of the data and highlight the complexity of the Pattern-of-Life related tasks

: Flex-Mediation

International audienceComprendre les médiations entre productions variables et utilisations évolutives de l'électricité. Présentation du projet de recherche en sciences humaines et sociales 2023-2028 en 5 axes : - Nouveaux arrangements organisationnels entre production et consommation et leur mode de valorisation des ressources- Influence des modes d’émergence et de fonctionnement des communautés énergétiques sur le juste partage des coûts et bénéfices- Influence des intermédiaires sur la perception de la variabilité des ENR et les pratiques des consommateurs- Conception des politiques publiques et de la régulation relative à la flexibilité : nouvelles formes de services (fournisseurs, agrégateurs, communautés) et intégration dans les mécanismes de marchés, comparaison internationale- Traduction légale de la flexibilité et questions de justice énergétique : comparaison européenne, analyse contractuelle multi-échelles, innovations en droits spécifiques pour les acteurs concerné