US 12,409,947 B2
System and method for detecting aircraft energy anomaly using neural network model
Noh-Sam Park, Daejeon (KR); Ji Yeon Kim, Daejeon (KR); and Jong Hyun Jang, Daejeon (KR)
Assigned to Electronics and Telecommunications Research Institute, Daejeon (KR)
Filed by ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE, Daejeon (KR)
Filed on Jul. 10, 2023, as Appl. No. 18/349,321.
Claims priority of application No. 10-2022-0119982 (KR), filed on Sep. 22, 2022; and application No. 10-2023-0055295 (KR), filed on Apr. 27, 2023.
Prior Publication US 2024/0101272 A1, Mar. 28, 2024
Int. Cl. B64D 45/00 (2006.01); G06N 3/0455 (2023.01); G08G 5/54 (2025.01); G06N 3/08 (2023.01)
CPC B64D 45/00 (2013.01) [G06N 3/0455 (2023.01); G08G 5/54 (2025.01); B64D 2045/0085 (2013.01)] 14 Claims
OG exemplary drawing
 
1. A system for detecting an aircraft energy anomaly using an artificial neural network learning model, the system comprising:
an input interface device for receiving ADS-B (Automatic Dependent Surveillance-Broadcast) data;
a memory storing a program which generates a specific energy feature for energy state analysis by extending the ADS-B data through a preprocessing; and
a processor for executing the program,
wherein the specific energy feature relates to whether aircraft energy is too high or too low during an approach stage of an aircraft to a runway, and
wherein the processor generates an energy distribution model by the use of the specific energy feature, and performs artificial neural network-based energy anomaly learning.