Page 11 - ITU-T Focus Group on Aviation Applications of Cloud Computing for Flight Data Monitoring - Key findings, recommendations for next steps and future work

ITU-T Focus Group on Aviation Applications of Cloud Computing for Flight Data Monitoring
Key findings, recommendations for next steps and future work

In addition, some information should only be transmitted upon certain triggering conditions, and in that case,
vital information should be sent first. All the processing power and applications that are needed in order to
accomplish the transmission, data acquisition and analysis must run in an on-board device.

3.1.2.3 Video analytics

Video analytics is defined as the collection and detection of abnormal behaviour, movement or events via
video streaming. With the advancement of data analytics, the analysis and detection of abnormal behaviour
or movement using real-time video analytics can provide a proactive source of data for FDM. For example,
the typical abnormal behaviour or events includes falling, running, tussling, entering restricted zones, etc.,
unwanted events that are defined by the airline industry. The abnormal event detection is followed by the
generation of a triggered signal data in real time. The transmission of the triggered event to the ground system
or cloud services for air traffic management/operations serves as an emergency alert. The recordings made on
the ground systems and the video analytics also provide digital evidence (digital forensics) in understanding
the causes of accidents and for post-flight operation management. The timely and real-time availability of
video data for any incidents that may result in an accident, crash or loss of aircraft can be designed for better
and safer flight operation. For example, the discovery of human factors that compromises a flight can help
provide clarity in accident investigations.

Thus, the benefits of video analytics are:

1) Make it easier to locate an aircraft in case of an emergency.
2) Improve accurate search and rescue response that would significantly reduce the search and rescue
efforts and costs in determining the location of an accident site.

3.1.2.4 Machine learning and quantum computing

Machine learning is a subfield of computer science driven by computational thinking that evolved from the
study of pattern recognition and computational learning theory in artificial intelligence. Machine learning
explores the construction and study of algorithms that can learn from and make predictions on data. Such
algorithms operate by building a model from example inputs in order to make data-driven predictions or
decisions, rather than following strictly static program instructions.

When employed in the aviation industry, machine learning methods may be referred to as predictive analytics
or predictive modelling.

Quantum computing studies theoretical computation systems (quantum computers) that make direct use of
quantum-mechanical phenomena, such as superposition and entanglement, to perform operations on data.
Quantum computers are different from digital computers based on transistors. Whereas digital computers
require data to be encoded into binary digits (bits), each of which is always in one of two definite states (0
or 1), quantum computation uses quantum bits (qubits), which can be in superposition of states. Large-scale
quantum computers will be able to solve certain problems much more quickly than any classical computers
that use even the best currently known algorithms, like integer factorization using Shor's algorithm or the
simulation of quantum many-body systems.

3.1.2.5 Digital asset profile system

Digital asset profile system enables applications to interact with physical objects by a unique identity for a
physical object (e.g. an aircraft component) and associated information (e.g. performance, maintenance) and
maintain a record of its lifetime in operation (e.g. usage, quality, value). The platform provides a simple way
to access the asset information and interpret it in order to support the process or operation being executed.
The profile information also enables early identification of problems, analysing situations and early detection
of deviations from the expected operations.

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