Laboratoire de recherche ENAC

Operators of complex systems across multiple domains (e.g., aviation, automotive, and nuclear power industry) are required to perform their tasks over prolonged and continuous periods of time. Mental fatigue as well as reduced cognitive flexibility, attention, and situational awareness all result from prolonged continuous use, putting at risk the safety and efficiency of complex operations. Mental state-based adaptive systems may be a solution to this problem. These systems infer the current mental state of an operator based on a selection of metrics ranging from operator independent measures (e.g., weather and time of day), to behavioral (e.g., reaction time and lane deviation) as well as physiological markers (e.g., electroencephalography and cardiac activity). The interaction between operator and system may then be adapted in one of many ways to mitigate any detected degraded cognitive state, thereby ensuring continued safety and efficiency. Depending on the task at hand and its specific problems, possible adaptations -usually based on machine learning estimations- e.g., include modifications of information, presentation modality or stimuli salience, as well as task scheduling. Research on adaptive systems is at the interface of several domains, including neuroergonomics, human factors, and human-computer interaction in an applied and ecological context, necessitating careful consideration of each of the aforementioned aspects. This article provides an overview of some of the key questions and aspects to be considered by researchers for the design of mental state-based adaptive systems, while also promoting their application during prolonged continuous use to pave the way toward safer and more efficient human-machine interaction.

A crucial step in inbred plant breeding is the choice of mating design to derive high-performing inbred varieties while also maintaining a competitive breeding population to secure sufficient genetic gain in future generations. In practice, the mating design usually relies on crosses involving the best parental inbred lines to ensure high mean progeny performance. This excludes crosses involving lower performing but more complementary parents in terms of favorable alleles. We predicted the ability of crosses to produce putative outstanding progenies (high mean and high variance progeny distribution) using genomic prediction models. This study compared the benefits and drawbacks of 7 genomic cross selection criteria (CSC) in terms of genetic gain for 1 trait and genetic diversity in the next generation. Six CSC were already published, and we propose an improved CSC that can estimate the proportion of progeny above a threshold defined for the whole mating plan. We simulated mating designs optimized using different CSC. The 835 elite parents came from a real breeding program and were evaluated between 2000 and 2016. We applied constraints on parental contributions and genetic similarities between selected parents according to usual breeder practices. Our results showed that CSC based on progeny variance estimation increased the genetic value of superior progenies by up to 5% in the next generation compared to CSC based on the progeny mean estimation (i.e. parental genetic values) alone. It also increased the genetic gain (up to 4%) and/or maintained more genetic diversity at QTLs (up to 4% more genic variance when the marker effects were perfectly estimated).

In modern GNSS receivers, the Automatic Gain Control (AGC) monitors the received signal level to optimize quantization and mitigate interference. This paper characterizes the jamming and spoofing impact on AGC and received signal. It first expresses the AGC gain as a function of the received signal level. Under nominal conditions, the AGC leverages the ergodic properties of the received signal to estimate its level over time. Two physical quantities, namely time-based power and signal distribution, are typically considered. However, in the presence of interference, these ergodic properties are no longer guaranteed, posing challenges in modeling the behavior of these quantities. This paper proposes a probabilistic framework for interpreting temporal estimation and computing time-based power and distribution in order to characterize AGC gain under jamming and spoofing. First, this study models the spoofing impact for both unique and multiple emitted spoofing signals as a function of the re-radiated noise power and the spoofing signals’ characteristics (e.g., number of emitted signals, amplitudes, modulation). Furthermore, it reveals the non-uniformity of the jamming chirp phase, which introduces distortions in power and signal distribution, consequently affecting AGC gain and demonstrates the convergence of the jamming signal toward a continuous wave signal at high frequencies. • Expression of the AGC dynamic behavior as a function of the received signal level. • Definition of a new probabilistic framework to interpret time-based estimation. • Approximation of multiple spoofing signals into a Gaussian model. • Highlighting the non-uniformity and non-ergodicity of chirp phase. • Approximation of the chirp signal as a continuous wave at intermediate frequency.

Metropolis II aims to provide insights in what is needed to enable high-density urban air operations. It does this by investigating the foundation for U-space U3/U4 services. The final goal is to provide a unified approach for strategic deconfliction, tactical deconfliction, and dynamic capacity management. Highly-dense operations in constrained urban airspace will likely require a degree of complexity that does not exist in modern-day air traffic management. The expected high traffic demand will require a shared use of the airspace instead of assigning exclusive use of blocks of the airspace to some flights. A unified approach for traffic management is needed because at high-densities, airspace design, flight planning, and separation management become increasingly interdependent. Metropolis II builds upon the results of the first Metropolis project. Three concepts with a varying degree of centralisation will be compared using simulations. (1) The centralised concept will take a global approach for separation management. (2) The decentralised concept aims to give the individual agents separation responsibility. (3) The hybrid concept tries to combine a centralised strategic planning agent with a robust tactical separation strategy.

View Video Presentation: https://doi.org/10.2514/6.2022-1150.vid Wind energy production is still a challenge, and improving the engineering practices in wind turbine design is crucial to overcome it. In a control co-design approach, as opposed to sequential design, one considers the interaction between subsystems and applies control concepts in the design process to obtain an optimal solution. This paper investigates control co-design optimization of a wind turbine blade and tower, where the power efficiency is aimed to be maximized while minimizing the system's total mass. Furthermore, bounds on the design parameters are defined to constraint structural load to admissible values. The power efficiency can be improved by reducing the root mean squared value of the generator speed tracking error, and the mass can be modified by the tower thickness. A mixed control architecture of H∞ and reference governors is proposed to design the wind turbine control system. The H∞ technique reduces structural loads, and the use of reference governors improves the system's power efficiency and addresses actuator limitations. Finally, the benefits of the control co-design optimization method are shown in the conclusion and future work is discussed.

We develop a two-stage game in which competing airlines first choose the networks of markets to serve in the first stage before competing in price in the second stage. Spillovers in entry decisions across markets are allowed, which accrue on the demand, marginal cost, and fixed cost sides. We show that the second-stage parameters are point identified, and we design a tractable procedure to set identify the first-stage parameters and to conduct inference. Further, we estimate the model using data from the domestic US airline market and find significant spillovers in entry. In a counterfactual exercise, we evaluate the 2013 merger between Amer- ican Airlines and US Airways. Our results highlight that spillovers in entry and post-merger network readjustments play an important role in shaping post-merger outcomes.

Summary This study investigates the unsplittable multicommodity flow (UMCF) as a routing algorithm for LEO constellations. Usually, LEO routing schemes enable the Floyd–Warshall algorithm (shortest path) to minimize the end‐to‐end latency of the flows crossing the constellation. We propose to solve the UMCF problem associated with the system as a solution for routing over LEO. We use a heuristic algorithm based on randomized rounding known in the optimization literature to efficiently solve the UMCF problem. Furthermore, we explore the impact of choosing the first/last hop before entering/exiting the constellation. Using network simulation over Telesat constellation, we show that UMCF maximizes the end‐to‐end links usage, providing better routing while minimizing the delay and the congestion level, which is an issue today over new megaconstellations.

Abstract A crucial step in inbred plant breeding is the choice of mating design to derive high-performing inbred varieties while also maintaining a competitive breeding population to secure sufficient genetic gain in future generations. In practice, the mating design usually relies on crosses involving the best parental inbred lines to ensure high mean progeny performance. This excludes crosses involving lower performing but more complementary parents in terms of favorable alleles. We predicted crosses with putative outstanding progenies (high mean and high variance progeny distribution) using genomic prediction models to assess the value of top progeny. This study compared the benefits and drawbacks of seven genomic cross selection criteria (CSC) in terms of genetic gain for one trait and genetic diversity in the next generation. Six CSC were already published and we have proposed an improved CSC that can estimate the proportion of progeny above a threshold defined for the whole mating plan. We simulated mating designs optimized using different CSC and 835 elite parents from a real breeding program that were evaluated between 2000 and 2016. We applied constraints on parental contributions and genetic similarities between parents according to usual breeder practices. Our results showed that CSC based on progeny variance estimation increased the genetic value of superior progenies by up to 5% in the next generation compared to CSC based on the progeny mean estimation (i.e. parental genetic values) alone. It also increased the genetic gain (up to 4%) and/or maintained more genetic diversity at QTLs (up to 4% more genic variance when the marker effects were perfectly estimated).

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International audience

International audience

International audience

International audience

International audience

International audience

International audience

International audience

International audience

International audience