Please use this identifier to cite or link to this item: http://repository.kln.ac.lk/handle/123456789/20160
Full metadata record
DC FieldValueLanguage
dc.contributor.authorShibata, M.-
dc.contributor.authorTakahashi, M.-
dc.date.accessioned2019-05-13T04:07:47Z-
dc.date.available2019-05-13T04:07:47Z-
dc.date.issued2019-
dc.identifier.citationShibata, M. and Takahashi, M. (2019). A Novel Computational Method to Capture FPGA Technology Trends from Patent Information. IEEE International Research Conference on Smart computing & Systems Engineering (SCSE) 2019, Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka.P.91en_US
dc.identifier.urihttp://repository.kln.ac.lk/handle/123456789/20160-
dc.description.abstractThis paper provides a novel trend analysis of FPGA development with Machine Learning. Recently, demands for the computing power are expanding due to reform of industrial structure such as the Industry 4.0 and the explosive expansion of AI. In this paper, we reveal the technical development trend of the leading FPGA companies from the patent information with Machine Learning. We focus on the classification codes in the patent and employ Link Mining method as the analytical method. Link Mining is a conventional method to analyze the structural features of things. It simplifies the objects and the relations as the nodes and the edges. With the proposed method, we succeed in revealing the companies’ focused technology fields, the transition of focusing areas, and their differences and common points from the results of extracting the graphs’ featuresen_US
dc.language.isoenen_US
dc.publisherIEEE International Research Conference on Smart computing & Systems Engineering (SCSE) 2019, Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lankaen_US
dc.subjectField Programmable Gate Arrayen_US
dc.subjectMachine Learningen_US
dc.subjectSystem-On-a-Chipen_US
dc.titleA Novel Computational Method to Capture FPGA Technology Trends from Patent Information.en_US
dc.typeArticleen_US
Appears in Collections:Smart computing & Systems Engineering - (SCSE - 2019)

Files in This Item:
File Description SizeFormat 
SC-1 (14).pdf2.01 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.