Zobrazeno 1 - 10
of 3 614
pro vyhledávání: '"Ammar N"'
Autor:
Abbas, Ammar N., Mehak, Shakra, Chasparis, Georgios C., Kelleher, John D., Guilfoyle, Michael, Leva, Maria Chiara, Ramasubramanian, Aswin K
This study presents a novel methodology incorporating safety constraints into a robotic simulation during the training of deep reinforcement learning (DRL). The framework integrates specific parts of the safety requirements, such as velocity constrai
Externí odkaz:
http://arxiv.org/abs/2407.02231
Autor:
Abbas, Ammar N., Amazu, Chidera W., Mietkiewicz, Joseph, Briwa, Houda, Perez, Andres Alonzo, Baldissone, Gabriele, Demichela, Micaela, Chasparis, Georgios G., Kelleher, John D., Leva, Maria Chiara
Publikováno v:
International Journal of Human-Computer Interaction, 2024
In complex industrial and chemical process control rooms, effective decision-making is crucial for safety and efficiency. The experiments in this paper evaluate the impact and applications of an AI-based decision support system integrated into an imp
Externí odkaz:
http://arxiv.org/abs/2402.13219
Publikováno v:
Data & Knowledge Engineering, 2023
The difficulty of identifying the physical model of complex systems has led to exploring methods that do not rely on such complex modeling of the systems. Deep reinforcement learning has been the pioneer for solving this problem without the need for
Externí odkaz:
http://arxiv.org/abs/2310.18811
Traditional controllers have limitations as they rely on prior knowledge about the physics of the problem, require modeling of dynamics, and struggle to adapt to abnormal situations. Deep reinforcement learning has the potential to address these prob
Externí odkaz:
http://arxiv.org/abs/2310.14788
Publikováno v:
Results in Engineering, Vol 23, Iss , Pp 102393- (2024)
Steel-concrete-steel (SCS) structural element solutions are rising due to their advantages over conventional reinforced concrete in terms of cost and strength. The impact of SCS sections with various core materials on the structural performance of co
Externí odkaz:
https://doaj.org/article/5c112870af544954824a0afaf4874729
Publikováno v:
Preprint: International Conference on Big Data Analytics and Knowledge Discovery Proceedings, 2022
An open research question in deep reinforcement learning is how to focus the policy learning of key decisions within a sparse domain. This paper emphasizes combining the advantages of inputoutput hidden Markov models and reinforcement learning toward
Externí odkaz:
http://arxiv.org/abs/2206.13433
Publikováno v:
In Results in Engineering September 2024 23
Autor:
Chidera Winifred Amazu, Joseph Mietkiewicz, Ammar N. Abbas, Houda Briwa, Andres Alonso Perez, Gabriele Baldissone, Micaela Demichela, Davide Fissore, Anders L. Madsen, Maria Chiara Leva
Publikováno v:
Data in Brief, Vol 53, Iss , Pp 110170- (2024)
These datasets contain measures from multi-modal data sources. They include objective and subjective measures commonly used to determine cognitive states of workload, situational awareness, stress, and fatigue using data collection tools such as NASA
Externí odkaz:
https://doaj.org/article/f4c9654a512d45eaa5eb3d1ad7cdf3d1
Autor:
Abbas, Ammar N., Moser, David
The device used in this work detects the objects over the surface of the water using two thermal cameras which aid the users to detect and avoid the objects in scenarios where the human eyes cannot (night, fog, etc.). To avoid the obstacle collision
Externí odkaz:
http://arxiv.org/abs/2106.07015
Perception of the lane boundaries is crucial for the tasks related to autonomous trajectory control. In this paper, several methodologies for lane detection are discussed with an experimental illustration: Hough transformation, Blob analysis, and Bir
Externí odkaz:
http://arxiv.org/abs/2106.07003