فهرست مطالب mohammmad siami araghi

This paper proposes a method based on the artificial intelligence reinforcement Q-learning algorithm and paired comparison technique to solve the problem of health monitoring devices shortage in airlines. In this research, Kish Airline destinations considered as a case study. By considering the importance of continuing air travels during covid-19 pandemic, one of the most effective ways for decreasing the risk of COVID-19 infections in air travels is establishing the health monitoring stations at the airport gates. In view of the enormous number of airports and airlines routes, nationwide coverage of them by health monitoring stations is unimaginable. Therefore, in this paper, artificial intelligence reinforcement Q-learning algorithm functionality in estimating the risk of infecting the COVID-19 virus was considered for the Kish Airline destinations. Furthermore, the optimal policy for the distribution of the health monitoring devices is designed based on the computational model.

This paper proposes a method based on the reinforcement Q-learning to solve the problem of fully autonomous optimal path planning of a space robot. By increasing the number of available satellites in earth’s orbits, designation, and implementation of satellite orbital servicing stations considered by researchers. Nowadays, by considering the advancement in robotics science, space robots could be chosen as a part of solution for maintaining the damaged satellites in earth’s orbits. Guidance, control, and navigation of space robots throughout docking and joint maneuvers need a high degree of precision. In this paper, reinforcement Q-learning algorithm functionality in path planning analyzed through various computational simulations. The finding results from computational simulations demonstrate the usefulness of the mentioned approach.