简介:Inthispaper,weproposeasusceptible-infected-susceptible(SIS)modeloncomplexnetworks,small-world(WS)networksandscale-free(SF)networks,tostudytheepidemicspreadingbehaviorwithtimedelaywhichisaddedintotheinfectedphase.Consideringtheuniformdelay,thebasicreproductionnumberR_0onWSnetworksandR_0onSFnetworksareobtainedrespectively.OnWSnetworks,ifR_0≤1,thereisadisease-freeequilibriumanditislocallyasymptoticallystable;ifR_0>1,thereisanepidemicequilibriumanditislocallyasymptoticallystable.OnSFnetworks,ifR_0≤1,thereisadisease-freeequilibrium;ifR_0>1,thereisanepidemicequilibrium.Finally,wecarryoutsimulationstoverifytheconclusionsandanalyzetheeffectofthetimedelayr,theeffectiverateλ,averageconnectivityandtheminimumconnectivitymontheepidemicspreading.
简介:Renderingoflarge-scaleforestscenesisachallengingtask,whosehighlygeometriccomplexitywillputheavyburdenoncurrentgraphicshardware.Whennavigatingthescene,theoverallvisualresultisgenerallyconsideredasthecoreconcern.Anewmethodisproposedinthispaperforlarge-scaleforestrenderingusingclusteringandmergingstrategies.Ourmethodimprovestherenderingeffectbyclusteringpolygonsaccordingtothepointinformationwithrelationtoneighbours.Afastforestrenderingsystemisdevelopedaccordingly.Therelativetechniquesinthesystemcanimprovethevisualqualityondemandofdifferentapplications.
简介:Thenoisedatainverticalcomponentrecordsof85seismicstationsinFujianProvinceduring2012isusedastheresearchobjectinthispaper.Thenoisedataisdividedintofiveminutesegmentstocalculatethepowerspectra.Thehighreferencelineandlowreferencelineofstationarethenidentifiedbydrawingaprobabilitydensityfunctiongraph(PDF)usingthepowerspectralprobabilitydensityfunction.Moreover,accordingtotheanomaliesofPDFgraphsin85seismicstations,theabnormalnoiseisdividedintofourcategories:droppedpacket,lownoise,highnoise,andmediannoiseanomalies.Afterwards,fourselectionmethodsarefoundbythehighorlownoisereferencelineofthestations,andthesystemofreal-timemonitoringofseismicnoiseisformedbycombiningthefourselectionmethods.Noiserecordsof85seismicstationsinFujianProvinceinJuly2013areselectedforverification,andtheresultsshowthattheanomalousnoise-recognitionsystemcouldreacha90%successrateatmoststationsandtheeffectofselectionareverygood.Therefore,itcouldbeappliedtotheseismicnoisereal-timemonitoringinstations.
简介:Terahertz(THz)radiation,whosefrequencyrangesfrom0.1THzto10.0THz,hasrichscience,butlimitedtechnology.Ithaslongbeenconsideredthelastremainingscientificgapintheelectromagneticspectrum.Farfrombeingfullyexploited,itoffersgreatopportunitiesinscience,innovation,newtechnology,andpotentialapplications.
简介:Inthispaper,weconsiderthestationaryprobabilityandfirst-passagetimeofbiasedrandomwalkon1Dchain,whereateachstepthewalkermovestotheleftandrightwithprobabilitiespandqrespectively(0p,q1,p+q=1).Wederiveexactanalyticalresultsforthestationaryprobabilityandfirst-passagetimeasafunctionofpandqforthefirsttime.Ourresultssuggestthatthefirst-passagetimeshowsadoublepower-lawF~(N-1)~γ,wheretheexponentγ=2forN<|p-q|~(-1)andγ=1forN>|p-q|~(-1).Ourstudyshedsusefulinsightsintothebiasedrandom-walkprocess.
简介:ThetraditionalorientedFASTandrotatedBRIEF(ORB)algorithmhasproblemsofinstabilityandrepetitionofkeypointsanditdoesnotpossessscaleinvariance.Inordertodealwiththesedrawbacks,amodifiedORB(MORB)algorithmisproposed.Inordertoimprovetheprecisionofmatchingandtracking,thispaperputsforwardanMOKalgorithmthatfusesMORBandKanade-Lucas-Tomasi(KLT).ByusingKalman,theobject’sstateinthenextframeispredictedinordertoreducethesizeofsearchwindowandimprovethereal-timeperformanceofobjecttracking.TheexperimentalresultsshowthattheMOKalgorithmcanaccuratelytrackobjectswithdeformationorwithbackgroundclutters,exhibitinghigherrobustnessandaccuracyondiversedatasets.Also,theMOKalgorithmhasagoodreal-timeperformancewiththeaverageframeratereaching90.8fps.
简介:ThepreviouspaperreportedanewderivativeintheEuleriandescriptioninflatspace—thegeneralizedcovariantderivativeofgeneralizedEuleriancomponentwithrespecttotime.ThispaperextendsthethoughtfromtheEuleriandescriptiontotheLagrangiandescription:onthebasisofthepostulateofcovariantforminvariabilityintimefield,wedefineanewderivativeintheLagrangiandescriptioninflatspace—thegeneralizedcovariantderivativeofgeneralizedLagrangiancomponentwithrespecttotime.Besides,thecovariantdifferentialtransformationgroupissetup.ThecovariantforminvariabilityofLagrangianspace-timeisascertained.
简介:ThispaperreportsanewderivativeintheEuleriandescriptioninflatspace-thegeneralizedcovariantderivativewithrespecttotime.Thefollowingcontentsareincluded:(a)therestrictedcovariantderivativewithrespecttotimeforEuleriancomponentisdefined;(b)thepostulateofthecovariantforminvariabilityintimefieldissetup;(c)thegeneralizedcovariantderivativewithrespecttotimeforgeneralizedEuleriancomponentisdefined;(d)thealgebraicstructureofthegeneralizedcovariantderivativewithrespecttotimeismadeclear;(e)thecovariantdifferentialtransformationgroupintimefiledisderived.TheseprogressesrevealthecovariantforminvariabilityofEulerianspaceandtime.