Archive ouverte de l'ENAC
Not a member yet
3458 research outputs found
Sort by
Delay estimation with a carrier modulated by a band-limited signal
No full textInternational audienceSince time-delay estimation is a fundamental task in various engineering fields, several expressions for the CRB and MLE have been developed over the past decades. In all of these previous studies, a common assumption was that the wave transmission process introduced an unknown phase component, which made it impossible to exploit the phase component related to the delay from the carrier signal. However, there are practical scenarios where this unknown phase can be estimated and compensated for, enabling the utilization of the delay phase component from the carrier signal. In this context, we provide a comprehensive treatment of this scenario, including the derivation of the MLE and the associated CRB. This approach allows us to analyze the impact of each signal component (carrier frequency and baseband signal) on the achievable MSE of delay estimation relative to the SNR. It also reveals five distinct regions of operations, in contrast to the well-known three
A New Multiobjective <i>A</i><sup>∗</sup> Algorithm With Time Window Applied to Large Airports
No full textInternational audienceCurrent airport ground operations, relying on single and fxed aircraft taxiing rules, struggle to handle dynamic trafc fow changes during peak fight times at large airports. Tis leads to inefcient taxiing routes, prolonged taxiing times, and high fuel consumption. Tis paper addresses these issues by proposing a new adaptive method for dynamic taxiway routing in airport ground operations. Tis method aims to reduce ground taxiing time and fuel consumption while ensuring the safety of aircraft taxiing. Tis study proposes a multiobjective A * algorithm with time windows which takes into account the allocation of resources on airport taxiways and introduces factors such as turning angles, dynamic turning speeds, and dynamic characteristics of the ground operations. Experiments conducted over the 10 busiest days in the history of Tianjin Binhai International Airport demonstrate that the algorithm excels in minimizing total taxiing time, difering only by 0.5% from the optimal solution. It also optimizes multiple objectives such as fuel consumption and operates at a solving speed approximately three orders of magnitude faster than the optimal solution algorithm, enabling real-time calculation of aircraft taxiing paths. Te results of the study indicate that the proposed multiobjective A * algorithm with time windows can efectively provide decision support for dynamic routing in airport ground operations.</div
Evaluating Post-Quantum Key Exchange Mechanisms for UAV Communication Security
No full textInternational audienceThe seamless integration of Unmanned Aerial Systems (UAS) into public airspace necessitates a robust UAV Traffic Management (UTM) system. Information security in communications plays a pivotal role in ensuring the operational safety of the UTM. Maintaining the integrity and authentication of unicast communications, such as command and video information, is imperative to thwart tampering and impersonation. Encryption emerges as an optimal solution for safeguarding such data. Efficient utilization of symmetrical encryption mandates the imple-mentation of secure key exchange mechanisms. However, intro-ducingAuthenticated Key Exchange (AKE) standards on diminutive Unmanned Aerial Vehicles (UAVs) encounters problems due to hardware limitations. To surmount this challenge, lightweight cryptographic algorithms tailored for resource-constrained hard-ware have been developed. Nonetheless, both lightweight and conventional cryptographic algorithms are susceptible to quantum computing threats. In response, post-quantum cryptographic primitives are undergoing standardization. This study assesses post-quantumKey Encapsulation Mechanism (KEM) candidates, namely CRYSTALS-Kyber, Hamming Quasi-Cyclic (HQC), and BIKE, on hardware resembling small UAVs. The evaluation of these algorithms focuses on computation time, considering real-time tasks during flights. The findings affirm the viability of employing unmodified standards on resource-constrained hardware concurrently with other priority tasks. Furthermore, quantum-vulnerable standards are also investigated to compare their performance against their more secure counterparts. All three post-quantum KEM candidates demonstrate usability in this environment, with CRYSTALS-Kyber being recommended for its superior performance, even surpassing certain prevailing standards
Extracting Lateral Deconfliction Actions from Historical ADS-B data with Median Regression
No full textInternational audienceAir traffic controllers (ATCO) work in an uncertain environment where they operate traffic with a double objective of minimizing flight times while ensuring safe separations, i.e., detecting and solving trajectory conflicts. They constantly face uncertainties because of variable weather conditions, aircraft speeds, or pilots response time. The development and acceptance of decision support tools to help controllers perform a safe separation must account for these uncertainties and align with operational practices to ensure satisfactory user adoption. In this paper, we build upon a previously published dataset of actual conflict resolutions based on historical ADS-B data and flight plans. In this previous work, we proposed a heuristic to detect deconfliction situations among aircraft persistently deviating from their intended route. Here, we extend our approach to previously overlooked areas of the data. In particular, we apply a KNN-Median regression approach to additional explanatory variables, and gain more insight in the way ATCO cope with potentially conflicting traffic. The most significant improvement is the ability to extract deconfliction situations leveraging "direct-to" instructions.</div
Two-stage approaches to solving the robust job-shop problem with uncertainty budget
No full textInternational audienc
A Physiological Study on the Impact of Aging on Cognitive Performance of Air Traffic Controllers
No full textInternational audienc
Spectral sequences arising from the gauge equation
No full textThe gauge equation is a generalization of the conjugacy relation for Koszul connection to 1bundle morphisms that are not isomorphisms. The existence of non-trivial solution to this equation, 2especially when duality is imposed upon related connections, gives important information about the 3geometry of the manifolds considered. In this article, we use the gauge equation to introduce spectral 4sequences that are further specialized to Hessian structures.
Climate-aware air traffic flow management optimization via column generation
No full textInternational audienceAviation is one of the global warming contributors. Its impact is due to CO 2 and non-CO 2 effects. Trajectory design is one of action levers for minimizing the environmental impact of air transportation. However, it affects the Air Traffic Management and should satisfy airspace constraints, especially airspace capacities. This paper proposes a climate-aware version of the Air Traffic Flow Management (ATFM), focusing on CO 2 and one particular non-CO 2 effect: condensation trails (contrails), although other non-CO 2 effects can be integrated. An ATFM optimization model is proposed, solved by a column generation approach. This problem is solved using different metrics, from simple to more complex and realistic ones. Numerical experiments are conducted both in the lateral case and when the cruise altitude becomes a decision variable. The impact of airspace capacities is also evaluated. The problem instances that are studied are built from realistic open-access data and made publicly available
Conception d'un média d'interaction pour faciliter l'interaction entre un robot et un humain
No full textInternational audienceLa téléopération de robots devient de plus en plus utilisée. Cependant, les modalités et les paradigmes d’interaction ne considèrent que rarement l’utilisation de dispositifs externes au robot, comme tablette ou téléphone. Nous proposons de définir un nouveau paradigme d’interaction humain-robot médiatisé via l’utilisation d’un tel dispositif externe. Ce type de média est disponible et familier à la plupart des utilisateurs. En outre, ce dispositif étant personnel, il devient possible d’envisager de configurer les interfaces et interactions en fonction des profils des utilisateurs. Dans ce papier, nous présentons nos recherches initiales dans cette direction. Après avoir défini les terminologies (adaptative, adaptable, modulable, etc.), nous présentons deux brainstormings menés avec desutilisateurs experts en utilisation de robots. Pour conclure, nous proposons des directions pour des recherches futures
Optimisation multi-disciplinaire en grande dimension pour l'éco-conception avion en avant-projet
No full textDescription: Ph.D on Gaussian Process kernels for Bayesian optimization in high dimension with mixed and hierarchical variables at ISAE-SUPAERO. Keywords: Gaussian process, Black-box optimization, Bayesian inference, Multidisciplinary design optimization, Mixed hierarchical and categorical inputs, Eco-friendly aircraft design. Nowadays, there is a significant and growing interest in improving the efficiency of vehicle design processes through the development of tools and techniques in the field of MDO. Specifically, in aerostructure design, aerodynamic and structural variables influence each other and have a joint effect on quantities of interest like weight or fuel consumption and, as such, MDO arises as a powerful tool for automatically making interdisciplinary trade-offs. In the aircraft design context, the process generally involves mixed continuous and categorical design variables. For instance, the size of an aircraft’s structural parts can be described using continuous variables, while discrete variables may include either integer variables, like the number of panels, or categorical variables, like cross-sections or material choices.The objective of this Philosophiae Doctor (Ph.D) thesis is to propose an efficient approach for optimizing a multidisciplinary black-box model when the optimization problem is constrained and involves a large number of mixed integer design variables (typically 100 variables). The targeted optimization approach, called EGO, is based on a sequential enrichment of an adaptive surrogate model and, in this context, GP surrogate models are one of the most widely used in engineering problems to approximate time-consuming high fidelity models. EGO is a heuristic BO method that performs well in terms of solution quality. However, like any other global optimization method, EGO suffers from the curse of dimensionality, meaning that its performance is satisfactory on lower dimensional problems, but deteriorates as the dimensionality of the optimization search space increases. For realistic aircraft design problems, the typical size of the design variables can even exceed 100 and, thus, trying to solve directly the problems using EGO is ruled out.The latter is especially true when the problems involve both continuous and categorical variables increasing even more the size of the search space. In this Ph.D thesis, effective parameterization tools are investigated, including techniques like partial least squares regression, to significantly reduce the number of design variables. Additionally, Bayesian optimization is adapted to handle discrete variables and high-dimensional spaces in order to reduce the number of evaluations when optimizing innovative aircraft concepts such as the “DRAGON” hybrid airplane to reduce their climate impact.De nos jours, un intérêt significatif et croissant pour am améliorer les processus de conception de véhicules s’observe dans le domaine de l’optimisation multidisciplinaire grâce au développement de nouveaux outils et de nouvelles techniques.Concrètement, en conception aérostructure, les variables aérodynamiques et structurelles s’influencent mutuellement et ont un effet conjoint sur des quantités d’intérêt telles que le poids ou la consommation de carburant. L’optimisation multidisciplinaire se présente alors comme un outil puissant pouvant effectuer des compromis inter-disciplinaires.Dans le cadre de la conception aéronautique, le processus multidisciplinaire implique généralement des variables de conception mixtes, continues et catégorielles. Par exemple, la taille des pièces structurelles d’un avion peut être décrite à l'aide de variables continues, le nombre de panneaux est associé à un entier et la liste des sections transverses ou le choix des matériaux correspondent à des choix catégoriels. L’objectif de cette thèseest de proposer une approche efficace pour optimiser un modèle multidisciplinaire boîte noire lorsque le problème d’optimisation est contraint et implique un grand nombre de variables de conception mixtes (typiquement 100 variables). L’approche d’optimisation bayésienne utilisée consiste en un enrichissement séquentiel adaptatif d’un métamodèle pour approcher l’optimum de la fonction objectif tout en respectant les contraintes. Les modèles de substitution par processus gaussiens sont parmi les plus utilisés dansles problèmes d’ingénierie pour remplacer des modèles haute fidélité coûteux en temps de calcul. L’optimisation globale efficace est une méthode heuristique d’optimisation bayésienne conçue pour la résolution globale de problèmes d’optimisation coûteux à évaluer permettant d’obtenir des résultats de bonne qualité rapidement. Cependant, comme toute autre méthode d’optimisation globale, elle souffre du fléau de la dimension,ce qui signifie que ses performances sont satisfaisantes pour les problèmes de faible dimension, mais se détériorent rapidement à mesure que la dimension de l’espace de recherche augmente. Ceci est d’autant plus vrai que les problèmes de conception de systèmes complexes intègrent à la fois des variables continues et catégorielles, augmentant encore la taille de l’espace de recherche. Dans cette thèse, nous proposons des méthodes pour réduire de manière significative le nombre de variables de conception comme, par exemple, des techniques d’apprentissage actif telles que la régression par moindres carrés partiels. Ainsi, ce travail adapte l’optimisation bayésienne aux variables discrètes et à la grande dimension pour réduire le nombre d'évaluations lors de l’optimisation de concepts d’avions innovants moins polluants comme la configuration hybride électrique “DRAGON”