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Localized model to segmentally estimate miles per gallon (MPG) for equipment engines

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dc.contributor.authorLuo, J L
dc.contributor.authorLuo, Haojing
dc.contributor.authorLi, A M
dc.contributor.authorWang, H H
dc.coverage.spatialShanghai China
dc.date.accessioned2015-12-13T22:18:12Z
dc.date.createdApril 9-10 2014
dc.date.issued2014
dc.date.updated2015-12-11T07:42:08Z
dc.description.abstractIn this paper, we built a localized regression model to estimate the miles per gallon (MPG) characteristic for equipment engines based on a serious physical features of this engine. First, we statistically viewed these parameters to build up a basic understanding of the data we collected. Then, with the belief that engines with similar characteristics will perform similarly, we proposed a novel localized model with a novel optimal function based EM algorithm and a novel self-adjusted optimal clustering algorithm to estimate MPG based on the other fully studied engines with similar physical features.
dc.identifier.isbn9783038351153
dc.identifier.urihttp://hdl.handle.net/1885/71529
dc.publisherTrans Tech Publications
dc.relation.ispartofseries2014 International Conference on Mechatronics Engineering and Computing Technology, ICMECT 2014
dc.sourceApplied Mechanics and Materials
dc.titleLocalized model to segmentally estimate miles per gallon (MPG) for equipment engines
dc.typeConference paper
local.bibliographicCitation.lastpage1074
local.bibliographicCitation.startpage1069
local.contributor.affiliationLuo, J L, Academy of Armored Forces Engineering
local.contributor.affiliationLuo, Haojing, College of Business and Economics, ANU
local.contributor.affiliationLi, A M, Academy of Armored Forces Engineering
local.contributor.affiliationWang, H H, Carnegie Mellon University
local.contributor.authoruidLuo, Haojing, u5428418
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor150302 - Business Information Systems
local.identifier.absfor099999 - Engineering not elsewhere classified
local.identifier.ariespublicationU3488905xPUB2755
local.identifier.doi10.4028/www.scientific.net/AMM.556-562.1069
local.identifier.scopusID2-s2.0-84902095788
local.identifier.thomsonID000349448501096
local.type.statusPublished Version

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