Paradkar, RA, Hunter, IC, Somjit, N orcid.org/0000-0003-1981-2618 et al. (3 more authors) (2019) Investigation of Stored Energy Distribution in Filters Using K-Means Clustering Algorithm. In: Proceedings of the 49th European Microwave Conference. 2019 49th European Microwave Conference (EuMC), 01-03 Oct 2019, Paris, France. IEEE , pp. 396-399. ISBN 978-2-87487-055-2
Abstract
The k-means clustering algorithm has been implemented to find patterns in the time-averaged stored energy distribution in various filter networks. A large data set comprising of numerous topologies for 50 different single band specifications has been investigated. By finding key characteristics within this data set, general guidelines for predicting the optimum topology for power handling have been established.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 EuMA. This is an author produced version of a paper accepted for publication for 2019 49th European Microwave Conference (EuMC). Uploaded in accordance with the publisher's self-archiving policy. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Stored energy distribution, RF filters, k-means algorithm, pattern recognition |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Pollard Institute (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 26 Apr 2019 09:46 |
Last Modified: | 30 Jun 2020 14:49 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.23919/EuMC.2019.8910849 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:145127 |