Abstract
No public abstract is available from the verified source records.
Ángel-Grover Pérez-Muñoz, Hernán García-Quijano, Guillermo López-García, A. Alonso, María S. Pérez
Abstract
Authors
Institutions
Provenance
No local reference links have been materialized yet.
No local citing links have been materialized yet.
Assuring the machine learning lifecycle: Desiderata, methods, and challenges
10.1145/3453444 · 2021
Unresolved referenced work
2020
Unresolved referenced work
2026
Towards a continuous certification of safety-critical avionics software
10.1016/j.compind.2020.103382 · 2021
Application of deep reinforcement learning to defense and intrusion strategies using unmanned aerial vehicles in a versus game
10.3390/drones8080365 · 2024
Towards certification of a reduced footprint ACAS-Xu system: A hybrid ML-based solution
2021
ACETONE: Predictable programming framework for ML applications in safety-critical systems
2022
An introduction to the current state of standardization and certification on military AI applications
10.1016/j.jairtraman.2024.102685 · 2024
Unresolved referenced work
2023
Unresolved referenced work
2023
crossref
Confidence 100%
openalex
Confidence 95%
datacite
Confidence 0%
Unresolved referenced work
2024
Unresolved referenced work
2020
Unresolved referenced work
2024
Unresolved referenced work
2024
Unresolved referenced work
2025
Unresolved referenced work
2016
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
2018
Safe automotive software
2011
Unresolved referenced work
2018
Toward a machine learning development lifecycle for product certification and approval in aviation
10.4271/01-15-02-0009 · 2022
Reinforcement learning for altitude hold and path planning in a quadcopter
2020
RLOps: Development life-cycle of reinforcement learning aided open RAN
2022
Deep drone racing: From simulation to reality with domain randomization
10.1109/tro.2019.2942989 · 2020
Deep reinforcement learning for drone delivery
10.3390/drones3030072 · 2019
Unresolved referenced work
2025
Unresolved referenced work
2019
A practical reinforcement learning implementation approach for continuous process control
10.1016/j.compchemeng.2023.108232 · 2023
Safety assurance of machine learning for autonomous systems
10.1016/j.ress.2025.111311 · 2025
Challenges of machine learning applied to safety-critical cyber-physical systems
10.3390/make2040031 · 2020
Development and validation of a safe reinforcement learning drone controller
10.17979/ja-cea.2025.46.12154 · 2025
Feasibility of deep reinforcement learning for the real-time attitude control of a satellite system
10.1016/j.sysarc.2025.103513 · 2025
Unresolved referenced work
2021
Unresolved referenced work
2024
Unresolved referenced work
2024
Low-level memory and timing analysis of flight code for unmanned aerial systems
10.1007/s42401-024-00279-0 · 2024
AirSim: High-fidelity visual and physical simulation for autonomous vehicles
2018
A soft-kill reinforcement learning counter unmanned aerial system (C-UAS) with accelerated training
10.1109/access.2023.3253481 · 2023
Unresolved referenced work
1996
Unresolved referenced work
2018
How to certify machine learning based safety-critical systems? A systematic literature review
10.1007/s10515-022-00337-x · 2022
UAV path planning and obstacle avoidance based on reinforcement learning in 3D environments
10.3390/act12020057 · doi-reference
How to certify machine learning based safety-critical systems? A systematic literature review
10.1007/s10515-022-00337-x · doi-reference
A soft-kill reinforcement learning counter unmanned aerial system (C-UAS) with accelerated training
10.1109/access.2023.3253481 · doi-reference
Low-level memory and timing analysis of flight code for unmanned aerial systems
10.1007/s42401-024-00279-0 · doi-reference
Feasibility of deep reinforcement learning for the real-time attitude control of a satellite system
10.1016/j.sysarc.2025.103513 · doi-reference
Development and validation of a safe reinforcement learning drone controller
10.17979/ja-cea.2025.46.12154 · doi-reference
Challenges of machine learning applied to safety-critical cyber-physical systems
10.3390/make2040031 · doi-reference
Safety assurance of machine learning for autonomous systems
10.1016/j.ress.2025.111311 · doi-reference
A practical reinforcement learning implementation approach for continuous process control
10.1016/j.compchemeng.2023.108232 · doi-reference
Deep reinforcement learning for drone delivery
10.3390/drones3030072 · doi-reference
Deep drone racing: From simulation to reality with domain randomization
10.1109/tro.2019.2942989 · doi-reference
Toward a machine learning development lifecycle for product certification and approval in aviation
10.4271/01-15-02-0009 · doi-reference
An introduction to the current state of standardization and certification on military AI applications
10.1016/j.jairtraman.2024.102685 · doi-reference
Application of deep reinforcement learning to defense and intrusion strategies using unmanned aerial vehicles in a versus game
10.3390/drones8080365 · doi-reference
Towards a continuous certification of safety-critical avionics software
10.1016/j.compind.2020.103382 · doi-reference
Assuring the machine learning lifecycle: Desiderata, methods, and challenges
10.1145/3453444 · doi-reference