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Aircraft cruise alternative trajectories generation: a mixed RRG-clustering approach
No full textWeather obstacles in the airspace can interfere with an aircraft's flight plan. Pilots, assisted by air traffic controllers (ATCs), perform avoidance maneuvers that can be optimized. This paper addresses the generation of alternative aircraft trajectories to resolve unexpected events.The authors propose a solution based on the RRG algorithm, K-means clustering, and Dynamic Time Warping (DTW) similarity metric to address the problem.The mixed algorithm succeeds in generating a set of paths with diversity in an obstacle constrained airspace between Paris-Toulouse and London-Toulouse airports.This tool could help to reduce the workload of pilots and ATCs when such a situation arises
Benefit of wind networking for aircraft arrival scheduling in terminal manoeuvring area
No full textInternational audienc
Multipath Parameters Estimation in Physically Based Synthetic Environment Using Robust Deep Neural Regression
No full textInternational audienc
Using convolutional neural networks to detect GNSS multipath
No full textInternational audienceGlobal Navigation Satellite System (GNSS) multipath has always been extensively researched as it is one of the hardest error sources to predict and model. External sensors are often used to remove or detect it, which transforms the process into a cumbersome data setup. Thus, we decided to only use GNSS correlator outputs to detect a large-amplitude multipath, on Galileo E1-B and GPS L1 C/A, using a convolutional neural network (CNN). This network was trained using 101 correlator outputs being used as a theoretical classifier. To take advantage of the strengths of convolutional neural networks for image detection, images representing the correlator output values as a function of delay and time were generated. The presented model has an F score of 94.7% on Galileo E1-B and 91.6% on GPS L1 C/A. To reduce the computational load, the number of correlator outputs and correlator sampling frequency was then decreased by a factor of 4, and the convolutional neural network still has an F score of 91.8% on Galileo E1-B and 90.5% on GPS L1 C/A
Blackbox optimization and surrogate models for machining free-form surfaces
No full textInternational audienceThis paper introduces an optimization model for machining free-form surfaces. It involves one categorical decision variable and continuous decision variables. Its objective function is partially separable. It is composed of two blackboxes: a clustering blackbox that outputs a partition of the surface into K zones, and K independent blackboxes, each of which outputs a machining time for a zone. This blackbox optimization problem is solved with the state-of-the-art software NOMAD. In order to improve the performance of the optimization process, we propose several surrogates of the machining-time blackboxes. Some of these surrogates are simple numerical approximations of the machining time, while one proposed surrogate is analytical, cheap to evaluate and exact for zones that are rectangles. Numerical experiments on two benchmark test surfaces show that our methodology outperforms other approaches from the literature. Although performances are strongly dependent on the topology of the test surfaces, the gains in machining time can go up to 40%
Evaluation of complexity and deliverability of IMRT treatment plans for breast cancer
No full textThis study aimed to predict the outcome of patient specific quality assurance (PSQA) in IMRT for breast cancer using complexity metrics, such as MU factor, MAD, CAS, MCS. Several breast cancer plans were considered, including LBCS, RBCS, LBCM, RBCM, left breast, right breast and the whole breast for both Edge and TrueBeam LINACS. Dose verification was completed by Portal Dosimetry (PD). The receiver operating characteristic (ROC) curve was employed to determine whether the treatment plans pass or failed. The area under the curve (AUC) was used to assess the classification performance. The correlation of PSQA and complexity metrics was examined using Spearman's rank correlation coefficient (R-s). For LINACS, the most suitable complexity metric was found to be the MU factor (Edge R-s = - 0.608, p < 0.01; TrueBeam R-s = - 0.739, p < 0.01). Regarding the specific breast cancer categories, the optimal complexity metrics were as follows: MAD (AUC = 0.917) for LBCS, MCS (AUC = 0.681) for RBCS, MU factor (AUC = 0.854) for LBCM and MAD (AUC = 0.731) for RBCM. On the Edge LINAC, the preferable method for breast cancers was MCS (left breast, AUC = 0.938; right breast, AUC = 0.813), while on the TrueBeam LINAC, it became MU factor (left breast, AUC = 0.950) and MCS (right breast, AUC = 0.806), respectively. Overall, there was no universally suitable complexity metric for all types of breast cancers. The choice of complexity metric depended on different cancer types, locations and treatment LINACs. Therefore, when utilizing complexity metrics to predict PSQA outcomes in IMRT for breast cancer, it was essential to select the appropriate metric based on the specific circumstances and characteristics of the treatment
Transport aérien et sociétés : influences croisées: Revue de littérature
No full textÀ l’image de ses aéroports, des espaces où se matérialisent une multitude d’enjeux, économiques, sociaux, environnementaux, le transport aérien, dans son ensemble, est profondément enchevêtré dans le fonctionnement des sociétés humaines actuelles. Au-delà du moyen de transport, de l’outil au service de la mobilité, l’activité aérienne a infiltré toutes les composantes de la vie en société. Elle est par certains égards l’un de ses piliers organisateurs, et même d’un mode vie à part entière. À l’heure d’une réflexion sur le devenir de l’aviation au prisme du dérèglement climatique, il s’agit de mesurer et d’établir une vue globale sur ses implications sociétales, tant celles-ci sont multiples, subtiles et essentielles. Nous proposons, sous la forme d'une revue de littérature, de mettre en lumière les références scientifiques s’étant intéressées de près ou de loin aux influences croisées entre transport aérien et sociétés. Il s’agit de mettre au jour les externalités de l’aviation sur les sociétés, c’est-à-dire ses effets externes, conscients ou inconscients, recherchés ou non. De l’autre, il convient de comprendre comment celle-ci appréhende les évolutions sociétales, de tous ordres, passées, présentes et futures.</div
