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Full-Text Articles in Engineering

Computationally Efficient Strand Eddy Current Loss Calculation In Electric Machines, Alireza Fatemi, Dan M. Ionel, Nabeel Demerdash, Dave A. Staton, Rafal Wrobel, Yew Chuan Chong Mar 2019

Computationally Efficient Strand Eddy Current Loss Calculation In Electric Machines, Alireza Fatemi, Dan M. Ionel, Nabeel Demerdash, Dave A. Staton, Rafal Wrobel, Yew Chuan Chong

Electrical and Computer Engineering Faculty Research and Publications

A fast finite element (FE) based method for the calculation of eddy current losses in the stator windings of randomly wound electric machines is presented in this paper. The method is particularly suitable for implementation in large-scale design optimization algorithms where a qualitative characterization of such losses at higher speeds is most beneficial for identification of the design solutions that exhibit the lowest overall losses including the ac losses in the stator windings. Unlike the common practice of assuming a constant slot fill factor s f for all the design variations, the maximum s f in the developed method is …


Computational Modeling Of Trust Factors Using Reinforcement Learning, C. M. Kuzio, A. Dinh, C. Stone, L. Vidyaratne, K. M. Iftekharuddin Jan 2019

Computational Modeling Of Trust Factors Using Reinforcement Learning, C. M. Kuzio, A. Dinh, C. Stone, L. Vidyaratne, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

As machine-learning algorithms continue to expand their scope and approach more ambiguous goals, they may be required to make decisions based on data that is often incomplete, imprecise, and uncertain. The capabilities of these models must, in turn, evolve to meet the increasingly complex challenges associated with the deployment and integration of intelligent systems into modern society. Historical variability in the performance of traditional machine-learning models in dynamic environments leads to ambiguity of trust in decisions made by such algorithms. Consequently, the objective of this work is to develop a novel computational model that effectively quantifies the reliability of autonomous …