Antonio Acernese

Orcid: 0000-0002-4349-6320

According to our database1, Antonio Acernese authored at least 13 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

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Bibliography

2024
Trajectory Planning Based on Model Predictive Control with Dynamic Obstacle Avoidance in Unstructured Environments.
Proceedings of the European Control Conference, 2024

Informed Hybrid A Star-based Path Planning Algorithm in Unstructured Environments.
Proceedings of the European Control Conference, 2024

2022
A fuzzy logic-based approach for fault diagnosis and condition monitoring of industry 4.0 manufacturing processes.
Eng. Appl. Artif. Intell., 2022

A Novel Reinforcement Learning-based Unsupervised Fault Detection for Industrial Manufacturing Systems.
Proceedings of the American Control Conference, 2022

2021
Model-Free Self-Triggered Control Co-Design for Probabilistic Boolean Control Networks.
IEEE Control. Syst. Lett., 2021

Reinforcement Learning Approach to Feedback Stabilization Problem of Probabilistic Boolean Control Networks.
IEEE Control. Syst. Lett., 2021

A Multi-Step Anomaly Detection Strategy Based on Robust Distances for the Steel Industry.
IEEE Access, 2021

Fault Detection and Diagnosis in Steel Industry: a One Class-Support Vector Machine Approach.
Proceedings of the 2021 IEEE International Conference on Systems, Man, and Cybernetics, 2021

Robust Statistics-based Anomaly Detection in a Steel Industry.
Proceedings of the 29th Mediterranean Conference on Control and Automation, 2021

A comparison of envelope and statistical analyses for bearing diagnosis in hot steel rolling mill lines.
Proceedings of the IECON 2021, 2021

2020
Double Deep-Q Learning-Based Output Tracking of Probabilistic Boolean Control Networks.
IEEE Access, 2020

2019
Condition Based Maintenance for Industrial Labeling Machine.
Proceedings of the 4th International Conference on System Reliability and Safety, 2019

A Combined Support Vector Machine and Support Vector Representation Machine Method for Production Control.
Proceedings of the 17th European Control Conference, 2019


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