Trajectory simulation for unguided artillery projectile

2015 ◽  
pp. 837-840
Keyword(s):  
Trajectory Simulation ◽  
Artillery Projectile

scholarly journals Deep Q-Learning for Two-Hop Communications of Drone Base Stations

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1960
Author(s):  
Azade Fotouhi ◽  
Ming Ding ◽  
Mahbub Hassan
Keyword(s):  
Degrees Of Freedom ◽  
Network Performance ◽  
Learning Model ◽  
Base Stations ◽  
Communication Model ◽  
Complex Environments ◽  
End User ◽  
Q Learning ◽  
Trajectory Simulation ◽  
Target Environment

In this paper, we address the application of the flying Drone Base Stations (DBS) in order to improve the network performance. Given the high degrees of freedom of a DBS, it can change its position and adapt its trajectory according to the users movements and the target environment. A two-hop communication model, between an end-user and a macrocell through a DBS, is studied in this work. We propose Q-learning and Deep Q-learning based solutions to optimize the drone’s trajectory. Simulation results show that, by employing our proposed models, the drone can autonomously fly and adapts its mobility according to the users’ movements. Additionally, the Deep Q-learning model outperforms the Q-learning model and can be applied in more complex environments.

Trajectory Simulation and Prediction of COVID-19 via Compound Natural Factor (CNF) Model in EDBF Algorithm

2021 ◽  
Author(s):  
Zhengkang Zuo ◽  
Sana Ullah ◽  
Yiyuan Sun ◽  
Fei Peng ◽  
Kaiwen Jiang
Keyword(s):  
Natural Factor ◽  
Trajectory Simulation

Experimental and Analytical Decentralized Adaptive Control of a 7-DOF Robot Manipulator

2020 ◽  
Author(s):  
Alexander Bertino ◽  
Peiman Naseradinmousavi ◽  
Atul Kelkar
Keyword(s):  
Adaptive Control ◽  
Control Strategy ◽  
Computational Efficiency ◽  
Tracking Control ◽  
Robot Manipulator ◽  
Problem Formulation ◽  
Experimental Results ◽  
Model Free ◽  
Trajectory Simulation ◽  
Adaptive Control Strategy

Abstract In this paper, we study the analytical and experimental control of a 7-DOF robot manipulator. A model-free decentralized adaptive control strategy is presented for the tracking control of the manipulator. The problem formulation and experimental results demonstrate the computational efficiency and simplicity of the proposed method. The results presented here are one of the first known experiments on a redundant 7-DOF robot. The efficacy of the adaptive decentralized controller is demonstrated experimentally by using the Baxter robot to track a desired trajectory. Simulation and experimental results clearly demonstrate the versatility, tracking performance, and computational efficiency of this method.

scholarly journals An Advanced Numerical Trajectory Model Tracks a Corn Earworm Moth Migration Event in Texas, USA

Insects ◽  
2018 ◽  
Vol 9 (3) ◽  
pp. 115 ◽  
Author(s):  
Qiu-Lin Wu ◽  
Gao Hu ◽  
John Westbrook ◽  
Gregory Sword ◽  
Bao-Ping Zhai
Keyword(s):  
Helicoverpa Zea ◽  
Wrf Model ◽  
Weather Conditions ◽  
Computational Design ◽  
Corn Earworm ◽  
Long Distance ◽  
Hysplit Model ◽  
Trajectory Model ◽  
Trajectory Simulation ◽  
Migration Event

Many methods for trajectory simulation, such as Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), have been developed over the past several decades and contributed greatly to our knowledge in insect migratory movement. To improve the accuracy of trajectory simulation, we developed a new numerical trajectory model, in which the self-powered flight behaviors of insects are considered and trajectory calculation is driven by high spatio-temporal resolution weather conditions simulated by the Weather Research and Forecasting (WRF) model. However, a rigorous evaluation of the accuracy of different trajectory models on simulated long-distance migration is lacking. Hence, in this study our trajectory model was evaluated by a migration event of the corn earworm moth, Helicoverpa zea, in Texas, USA on 20–22 March 1995. The results indicate that the simulated migration trajectories are in good agreement with occurrences of all pollen-marked male H. zea immigrants monitored in pheromone traps. Statistical comparisons in the present study suggest that our model performed better than the popularly-used HYSPLIT model in simulating migration trajectories of H. zea. This study also shows the importance of high-resolution atmospheric data and a full understanding of migration behaviors to the computational design of models that simulate migration trajectories of highly-flying insects.

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