Francisco Pérez Moreno

Assistant Professor

Universidad Politécnica de Madrid

Spain

Research Interests

Explore related searches

Professors in Spain

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Francisco Pérez Moreno is an Assistant Professor at Universidad Politécnica de Madrid, Spain. His research interests encompass artificial intelligence applications in aviation, focusing on AI-driven interference detection, security in airline transactions, and safety standards for military aviation. He has contributed to various topics including autonomous route planning, air traffic complexity, and advancements in battery technologies for electrifying aviation.

Articles (17)

Year:

2026

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Collaborators (3)

María Zamarreño Suárez

Resrarcher and Assistant Professor

Universidad Politécnica de Madrid

SPAIN

View details

ROSA MARIA ARNALDO VALDES

Universidad Politécnica de Madrid

SPAIN

View details

VICTOR FERNANDO GOMEZ COMENDADOR

Universidad Politécnica de Madrid

SPAIN

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Francisco Pérez Moreno

Research Interests

Explore related searches

Contact this professor

About

Articles (17)

AI-Driven Real-Time Interference Detection in Manned-Unmanned Aircraft Communications: Concept of Operations and Integration

Enhancing Security in Airline Ticket Transactions: A Comparative Study of SVM and LightGBM

Enhancing Safety in Military Aviation: A Systematic Approach to the Development of AI Certification Standards

Autonomous Route Planning for UAV-Based 3D Reconstruction

Battery Technologies for Use in Aviation: Classification, Temporal Evolution and Future Prospects

Prediction of air traffic complexity through a dynamic complexity indicator and machine learning models

How Has the Concept of Air Traffic Complexity Evolved? Review and Analysis of the State of the Art of Air Traffic Complexity

An introduction to the current state of standardization and certification on military AI applications

Establishing Rigorous Certification Standards: A Systematic Methodology for AI Safety-Critical Systems in Military Aviation

Prediction of Capacity Regulations in Airspace Based on Timing and Air Traffic Situation

Methodology for Determining the Event-Based Taskload of an Air Traffic Controller Using Real-Time Simulations

Battery Technologies to Electrify Aviation: Key Concepts, Technologies, and Figures

Methodology of air traffic flow clustering and 3-D prediction of air traffic density in ATC sectors based on machine learning models

Determination of Air Traffic Complexity Most Influential Parameters Based on Machine Learning Models

Dynamic model to characterise sectors using machine learning techniques

How much workload is workload? A human neurophysiological and affective-cognitive performance measurement methodology for ATCOs

Safety performance functions to predict separation minima infringements in en-route airspace

Collaborators (3)

María Zamarreño Suárez

ROSA MARIA ARNALDO VALDES

VICTOR FERNANDO GOMEZ COMENDADOR

Make your next move.