Airbus Group India Private Limited

Hewlett Packard Enterprise Development LP

Ashutosh Agrawal

Satish Kumar Mopur

Sridhar Balachandriah

Gunalan Perumal Vijayan

Saikat Mukherjee

Gregory S Battas

Krishnaprasad Lingadahalli Shastry

KrishnaPrasad Lingadahalli Shastry

Georges Myczkowski

Brendan Clarke

Nicolas Landrin

Lucas Babelaere

Nathalie Willig

Diego Alonso-Tabares

Anmol Pandey

The disclosure relates to a framework for dynamic management of analytic functions such as data processors and machine learned ('ML') models for an Internet of Things intelligent edge that addresses management of the lifecycle of the analytic functions from creation to execution, in production. The end user will be seamlessly able to check in an analytic function, version it, deploy it, evaluate model performance and deploy refined versions into the data flows at the edge or core dynamically for existing and new end points. The framework comprises a hypergraph-based model as a foundation, and may use a microservices architecture with the ML infrastructure and models deployed as containerized microservices.

Intelligent lifecycle management of analytic functions for an IOT intelligent edge with a hypergraph-based approach

A system and method for accounting for the impact of concept drift in selecting machine learning training methods to address the identified impact. Pattern recognition is performed on performance metrics of a deployed production model in an Internet-of-Things (IoT) environment to determine the impact that concept drift (data drift) has had on prediction performance. This concurrent analysis is utilized to select one or more approaches for training machine learning models, thereby accounting for the temporal dynamics of concept drift (and its subsequent impact on prediction performance) in a faster and more efficient manner.

Systems and methods for determining machine learning training approaches based on identified impacts of one or more types of concept drift

Intelligent lifecycle management of analytic functions for an IoT intelligent edge with a hypergraph-based approach

The disclosure relates to technology that implements flow control for machine learning on data such as Internet of Things ('IoT') datasets. The system may route outputs of a data splitter function performed on the IoT datasets to a designated target model based on a user specification for routing the outputs. In this manner, the IoT datasets may be dynamically routed to target datasets without reprogramming machine-learning pipelines, which enable rapid training, testing and validation of ML models as well as an ability to concurrently train, validate, and execute ML models.

Intelligent IOT data split management

A system and method for providing in-flight weather information to compute an optimized vertical flight profile are disclosed. In one embodiment, on-board weather information is obtained from one or more aircraft at regular intervals during flight. Further, weather information is identified for a predicted flight trajectory of an aircraft from the obtained on-board weather information. For example, the aircraft is preceding the at least one aircraft. Furthermore, a subset of the identified weather information is dynamically provided to a flight management system (FMS) in the aircraft, during flight, to compute the optimized vertical flight profile.

System and method for providing in-flight weather information to compute an optimized vertical flight profile

A system and method for aircraft cabin activity management occurring during turnaround using video analytics are disclosed. In one embodiment, real-time video feed of the aircraft cabin activities is obtained during the turnaround from at least one video camera disposed in an aircraft cabin. Further, aircraft cabin activity time stamps and progress associated with one or more aircraft cabin activities are determined by applying video analytics on the obtained video feed. Furthermore, the aircraft cabin activities are managed using the determined aircraft cabin activity time stamps and the progress associated with the one or more aircraft cabin activities.

Management of aircraft in-cabin activities occuring during turnaround using video analytics

A system for monitoring scheduled turnaround activities and alerting on time deviation from the scheduled turnaround activities is disclosed. The system includes a ground computing station system, an aircraft on-board system, a cloud, and user interface. The cloud is communicatively coupled to the ground station computing system and the aircraft on-board system. The cloud includes processor and memory. The memory includes an analytics module to obtain actual start and end time stamps associated with scheduled turnaround activities, from touchdown to takeoff of an aircraft, from aircraft on-board systems and a ground station system. The analytics module to determine time deviation of scheduled turnaround activities by analyzing the obtained actual start and end time stamps. The user interface to present each scheduled turnaround activity and determined time deviation of the scheduled turnaround activities.

Monitoring scheduled turnaround activities and alerting on time deviation of scheduled turnaround activities

A method for en-route flight path optimization is disclosed. A plurality of alternate flight paths between an origin and a destination of the aircraft in flight is determined based on real-time weather data, air traffic conflict data, and air space constraint data. Further, flight time savings and fuel savings for each of the plurality of alternate flight paths with respect to an actual path is computed. Furthermore, an optimal flight path from the plurality of alternate flight paths is determined based on the computed flight time savings, fuel savings, and a priority for the flight time savings and the fuel savings.

En-route flight path optimization

A system and method for airside activity management using video analytics are disclosed. In one embodiment, video data of a survey area is obtained, in real time, from one or more video cameras. Further, time stamps associated with one or more airside activities of an aircraft from touchdown to takeoff are determined by applying video analytics on the obtained video data to manage the one or more airside activities.

System and method for airside activity management using video analytics

A system and method for sending weather alerts to an aircraft during flight are disclosed. In one embodiment, in-flight weather information is obtained from one or more aircrafts at regular intervals. Further, weather conditions along a predicted flight trajectory of the aircraft are determined using the obtained in-flight weather information. The aircraft is preceding the one or more aircrafts. Furthermore, the weather alerts associated with the weather conditions are sent to a display in the aircraft during flight.

Growing community of inventors

Bengaluru, India

Ashutosh Agrawal