简介：

简介：Aimingattheproblemssuchasmorerepeatedlydesignandlongerdesigncycle,inthispaper,thesimilaritytheorywasintroducedtothedesignprocessofthekeystructuresofflotationmachine.TheimpellerandU-shapedtankofflotationmachinesystemwereanalyzedassimilarityunit.Meanwhile,thelevelofsimilarityoftheunitsandthesimilarityofthesystemwerecalculated.BasedontheanalysisoftheimpellerandthesizeofU-shapedtank,thesimilaritycriteriawerederived.Thederivedconclusionsare:(1)Therelationshipbetweenthediameteroftheimpellerandthevolumeofthetankwaspowerfunctionandcalculatedasthesimilaritycriteriaoftheimpeller;(2)TherelationshipbetweentheratiobetweentheU-shapedtank’scross-sectionalareaandimpeller’sdiameterandthevolumeofthetankwaspowerfunctionandcalculatedasthesimilaritycriterionsoftheU-shapedtank.Usingthesimilaritycriterioncombinedwithcomputertechnologyanddatabasetechnologytorealizepartandsystemserializationdesign.Theresultsshowthattheresearchcanavoidrepeatedlydesign,shortendesigncycle,andraisethedesignefficiency.

简介：ColumbiaMachine公司的新型高速在线货盘堆垛机型号为HL7200。堆垛速度为72～120箱／min（取决于纸箱类别）。该公司认为，它是工业主导型安全的货盘堆垛机，灵活性及性能都高。

简介：Withthewideapplicationofvirtualizationtechnologyinclouddatacenters,howtoeffectivelyplacevirtualmachine(VM)isbecomingamajorissueforcloudproviders.Theexistingvirtualmachineplacement(VMP)solutionsaremainlytooptimizeserverresources.However,theypaylittleconsiderationonnetworkresourcesoptimization,andtheydonotconcerntheimpactofthenetworktopologyandthecurrentnetworktraffic.Amulti-resourceconstraintsVMPschemeisproposed.Firstly,theauthorsattempttoreducethetotalcommunicationtrafficinthedatacenternetwork,whichisabstractedasaquadraticassignmentproblem;andthenaimatoptimizingnetworkmaximumlinkutilization(MLU).Ontheconditionofslightvariationofthetotaltraffic,minimizingMLUcanbalancenetworktrafficdistributionandreducenetworkcongestionhotspots,aclassiccombinatorialoptimizationproblemaswellasNP-hardproblem.Antcolonyoptimizationand2-optlocalsearcharecombinedtosolvetheproblem.SimulationshowsthatMLUisdecreasedby20%,andthenumberofhotlinksisdecreasedby37%.

简介：

简介：Supportvectormachines(SVM)receivedwideattentionforitsexcellentabilitytolearn,ithasbeenappliedinmanyfields.AreviewoftheapplicationofSVMinwelddefectdetectionandrecognitionofX-rayimageisbeenpresented.WewillshowsomecommonlyusedmethodsofwelddefectdetectionandrecognitionusingSVM,andtheadvantagesanddisadvantagesofeachmethodwillbediscussed.SVMappearstobepromisinginwelddefectdetectionandrecognition,butfutureresearchisneededbeforeitfullymatureinthisfiled.