... training datasets that may not always be accessible in practical applications. To address this issue, this work proposes a hybrid model consisting of a transformer neural network and a single particle model with electrolyte dynamics (SPMe) for SOC estimation in limited data scenarios. The transformer can...

... methods created to solve the eco-driving problem generally suffer from high computational and memory requirements, which makes online implementation challenging. This work proposes a hierarchical multi-horizon optimization framework implemented via a neural network. The neural network learns a full-route...

... , Mar. 5–12 , IEEE , pp. 1 – 12 . 10.1109/AERO53065.2022.9843777 [15] Shukla , D. , Lal , R. , Hauptman , D. , Keshmiri , S. S. , Prabhakar , P. , and Beckage , N. , 2020 , “ Flight Test Validation of a Safety-Critical Neural Network Based Longitudinal Controller...

...@villanova.edu Email: garrett.clayton@villanova.edu Email: c.nataraj@villanova.edu 18 04 2021 01 02 2022 13 02 2022 22 03 2022 machine learning neural networks This article presents a classification tool to assist an explosive ordnance disposal (EOD) technician...

... estimation machine learning neural networks pattern recognition and classification uncertain systems Deep neural networks are gaining popularity due to their ability to learn representations of data with multiple levels of abstraction [ 1 ]. In Ref. [ 2 ], researchers used deep learning to detect...

...Hailin Ren; Jingyuan Qi; Pinhas Ben-Tzvi This paper presents a method to imitate flatness-based controllers for mobile robots using neural networks. Sample case studies for a unicycle mobile robot and an unmanned aerial vehicle (UAV) quadcopter are presented. The goals of this paper are to (1...