公开(公告)号：US20220270497A1

公开(公告)日：2022-08-25

申请号：US17631486

申请日：2020-07-13

Applicant: THALES

Inventor： Béatrice PESQUET-POPESCU ,  Dominique LATGE ,  David LAVILLE ,  Fateh KAAKAI

IPC: G08G5/00 ,  G06N20/00

Abstract: A computation of the processing complexity of an air-traffic-control situation is provided. In particular, the processing complexity of an air-traffic-control situation is computed using a supervised learning engine, trained using the result of analytical functions for computing the processing complexity of air-traffic-control situations.

公开(公告)号：US20230316932A1

公开(公告)日：2023-10-05

申请号：US18008437

申请日：2021-05-11

Applicant: THALES

Inventor： Andrei PURICA ,  Béatrice PESQUET-POPESCU

IPC: G08G5/00

CPC classification number: G08G5/0026 ,  G08G5/0043 ,  G08G5/0082

Abstract: In the field of air traffic control, a method is provided to determine a processing complexity of an ATC situation. For this purpose, the method includes grouping parameters of the paths by pairs of paths in a matrix, applying to this matrix a transformation aiming to concentrate the energy, then calculating the complexity index of the ATC situation as a function of the concentration level of the energy per component.

公开(公告)号：US20230230490A1

公开(公告)日：2023-07-20

申请号：US18008436

申请日：2021-06-01

Applicant: THALES

Inventor： Paul LALISSE-BAUVIN ,  Béatrice PESQUET-POPESCU ,  Andrei PURICA ,  David LAVILLE

IPC: G08G5/06 ,  G08G5/00

CPC classification number: G08G5/065 ,  G08G5/0043

Abstract: A computer-implemented method is provided for training a supervised machine learning engine able to predict characteristics of aircraft trajectories from parameters of an aircraft, and environment parameters of the aircraft trajectory. A system able to train the supervised machine learning engine, a system for using the engine, and a computer-implemented method for using the engine are provided. The methods and systems provided are particularly useful for air traffic flow management applications.

公开(公告)号：US20220351629A1

公开(公告)日：2022-11-03

申请号：US17733861

申请日：2022-04-29

Applicant: THALES

Inventor： Béatrice PESQUET-POPESCU ,  Nicolas MARTIN ,  Olivier RÉA ,  Andrei PURICA

IPC: G08G5/00 ,  G08G5/04 ,  G06N3/08

Abstract: A computer-implemented method includes receiving a trajectory request from an unmanned aerial vehicle, the request comprising: an initial point; a final point; at least one manoeuvrability parameter of the unmanned aerial vehicle; computing a plurality of optimized 4D trajectories between the initial point and the final point, complying with the at least one manoeuvrability parameter, and avoiding obstacles in an airspace, each 4D trajectory being associated with a performance score; a flight simulator simulating the plurality of 4D trajectories in order of decreasing performance score, until a 4D trajectory is considered to be flyable by the flight simulator; sending the trajectory considered to be flyable by the flight simulator to the unmanned aerial vehicle.

公开(公告)号：US20220277201A1

公开(公告)日：2022-09-01

申请号：US17631487

申请日：2020-07-28

Applicant: THALES

Inventor： Andrei PURICA ,  Béatrice PESQUET-POPESCU

IPC: G06N3/08

Abstract: The embodiments of the invention provide a device for predicting the value of a variable intended to be used by a computer-implemented control system, the variable depending on multiple parameters, the parameters comprising a non-explicit parameter. Advantageously, the prediction device comprises a first neural network-based predictor configured so as to compute an estimate of the non-explicit parameter and a second neural network-based predictor configured so as to compute an estimate of the value of the variable from the estimate of the non-explicit parameter, the two predictors receiving an input dataset, each neural network being associated with a set of weights. The prediction device is configured so as to apply a plurality of iterations of a single learning function to the two predictors, the learning function comprising: a forward propagation block for computing, on the basis of the input data of the two predictors, the gradient of a minimization function for minimizing a cost function of the first predictor; and a backpropagation block for updating the weights of the neural networks of the two predictors by backpropagating the gradients computed by the forward propagation block. The prediction device estimates the value of the variable to be predicted at a future time, after the iterations of the learning function, by applying input data to the neural networks of the two predictors and using the weights updated by the learning function.