简介：Utilizingdatafromcontrolledseismicsourcestoimagethesubsurfacestructuresandinvertthephysicalpropertiesofthesubsurfacemediaisamajoreffortinexplorationgeophysics.Denseseismicrecordswithhighsignal-to-noiseratio(SNR)andhighfidelityhelpsinproducinghighqualityimagingresults.Therefore,seismicdatadenoisingandmissingtracesreconstructionaresignificantforseismicdataprocessing.Traditionaldenoisingandinterpolationmethodsrarelyoccasionedrelyonnoiselevelestimations,thusrequiringheavymanualworktodealwithrecordsandtheselectionofoptimalparameters.Weproposeasimultaneousdenoisingandinterpolationmethodbasedondeeplearning.Fornoisyrecordswithmissingtraces,weadoptaniterativealternatingoptimizationstrategyandseparatetheobjectivefunctionofthedatarestoringproblemintotwosub-problems.Theseismicrecordscanbereconstructedbysolvingaleast-squareproblemandapplyingasetofpre-traineddenoisingmodelsalternativelyanditeratively.Wedemonstratethismethodwithsyntheticandfielddata.

简介：Alargenumberofdebrisflowdisasters(calledSeismicdebrisflows)wouldoccurafteranearthquake,whichcancauseagreatamountofdamage.UAVlow-altituderemotesensingtechnologyhasbecomeameansofquicklyobtainingdisasterinformationasithastheadvantageofconvenienceandtimeliness,butthespectralinformationoftheimageissoscarce,makingitdifficulttoaccuratelydetecttheinformationofearthquakedebrisflowdisasters.Basedontheaboveproblems,aseismicdebrisflowdetectionmethodbasedontransferlearning(TL)mechanismisproposed.Onthebasisoftheconstructedseismicdebrisflowdisasterdatabase,thefeaturesacquiredfromthetrainingoftheconvolutionalneuralnetwork(CNN)aretransferredtothedisasterinformationdetectionoftheseismicdebrisflow.Theautomaticdetectionofearthquakedebrisflowdisasterinformationisthencompleted,andtheresultsofobject-orientedseismicdebrisflowdisasterinformationdetectionarecomparedandanalyzedwiththedetectionresultssupportedbytransferlearning.

简介：Thecombinationofvisualandtextualinformationinimageretrievalremarkablyalleviatesthesemanticgapoftraditionalimageretrievalmethods,andthusithasattractedmuchattentionrecently.Imageretrievalbasedonsuchacombinationisusuallycalledthecontent-and-textbasedimageretrieval(CTBIR).Nevertheless,existingstudiesinCTBIRmainlymakeeffortsonimprovingtheretrievalquality.Tothebestofourknowledge,littleattentionhasbeenfocusedonhowtoenhancetheretrievalefficiency.Nowadays,imagedataiswidespreadandexpandingrapidlyinourdailylife.Obviously,itisimportantandinterestingtoinvestigatetheretrievalefficiency.Tothisend,thispaperpresentsanefficientimageretrievalmethodnamedCATIRI(content-and-textbasedimageretrievalusingindexing).CATIRIfollowsathree-phasesolutionframeworkthatdevelopsanewindexingstructurecalledMHIM-tree.TheMHIM-treeseamlesslyintegratesseveralelementsincludingManhattanHashing,Invertedindex,andM-tree.TouseourMHIM-treewiselyinthequery,wepresentasetofimportantmetricsandrevealtheirinherentproperties.Basedonthem,wedevelopatop-kqueryalgorithmforCTBIR.ExperimentalresultsbasedonbenchmarkimagedatasetsdemonstratethatCATIRIoutperformsthecompetitorsbyanorderofmagnitude.

简介：Graphene,definedasasingleatomicplaneofgraphite,isasemimetalwithasmalloverlapbetweenthevalenceandconductionbands.Thestackingofgrapheneuptoseveralatomiclayerscanleadtodiversephysicalproperties,dependingonthestackingmethod.Bilayergrapheneisalsoasemimetal,adoptingtheAB-stacked(orBernal-stacked)structureortherareAA-stackedstructure.Trilayerorfew-layergraphene(FLG)canbesemimetalsorsemiconductors,dependingonwhethertheyadoptBernal(ABA)stackingorrhoinbohedral(ABC)stacking.

简介：Arobustmethodforcharacterizingthemineralogyofsuspendedsedimentincontinentalriversisintroduced.Itencompasses3steps:thefiltrationofafewmillilitersofwater,measurementsofX-rayenergydispersivespectrausingScanningElectronMicroscopy(SEM),androbustmachinelearningtoolsofclassification.ThemethodisappliedtosuspendedparticlescollectedfromvariousAmazonianrivers.Atotalofmorethan204,000particleswereanalyzedbySEM-EDXS(EnergyDispersiveX-raySpectroscopy),i.e.about15,700particlespersamplingstation,whichleadtotheidentificationof15distinctgroupsofmineralogicalphases.ThesizedistributionofparticlescollectedonthefilterswasderivedfromtheSEMmicrographstakeninthebackscatteredelectronimagingmodeandanalyzedwithImageJfreeware.Thedeterminationofthemainmineralogicalgroupscomposingthebulksedimentassociatedwithphysicalparameterssuchasparticlesizedistributionoraspectratioallowsaprecisecharacterizationoftheloadoftheterrigenousparticlesinriversorlakes.InthecaseoftheAmazonianriversinvestigated,theresultsshowthattheidentifiedmineralogiesareconsistentwithpreviousstudiesaswellasbetweenthedifferentsamplescollected.Themethodenabledtheevolutionofgrainsizedistributionfromfinetocoarsematerialtobedescribedinthewatercolumn.Implicationsabouthydrodynamicsortingofmineralparticlesinthewatercolumnarealsobrieflydiscussed.Theproposedmethodappearswellsuitedforintensiveroutinemonitoringofsuspendedsedimentinriversystems.

简介：Thisstudyinvestigatestheeffectivenessofthenon-smoothsemi-activecontrolalgorithmonsuppressingthevibrationperformanceofabuildingstructuresubjectedtoseismicwaves.AccordingtotheLyapunovstabilitytheory,ithasbeneproventhatthenon-smoothsemi-activecontrolalgorithmcanachieveafinite-timestabilityofthevibrationrelativetotheisolationlayerofabuildingstructure.Throughnumericalsimulationoftwobuildingswithdifferentparameterssubjectedtotheinputofaseismicwave,thevibrationconditionsofpassivecontrol,LQRsemi-activecontrolandnon-smoothsemiactivecontrolarecomparedandanalyzed.Thesimulationresultsshowthatthenon-smoothsemi-activecontrolalgorithmhasabetterrobustnessandeffectivenessinrestrainingtheimpactofearthquakesonthestructure.

简介：Asanimportantnon-ferrousmetalstructuralmaterialmostusedinindustryandproduction,aluminum(Al)alloyshowsitsgreatvalueinthenationaleconomyandindustrialmanufacturing.HowtoclassifyAlalloyrapidlyandaccuratelyisasignificant,popularandmeaningfultask.Classificationmethodsbasedonlaser-inducedbreakdownspectroscopy(LIBS)havebeenreportedinrecentyears.AlthoughLIBSisanadvanceddetectiontechnology,itisnecessarytocombineitwithsomealgorithmtoreachthegoalofrapidandaccurateclassification.Asanimportantmachinelearningmethod,therandomforest(RF)algorithmplaysagreatroleinpatternrecognitionandmaterialclassification.ThispaperintroducesarapidclassificationmethodofAlalloybasedonLIBSandtheRFalgorithm.TheresultsshowthatthebestaccuracythatcanbereachedusingthismethodtoclassifyAlalloysamplesis98.59%,theaverageofwhichis98.45%.ItalsorevealsthroughtherelationshiplawsthattheaccuracyvarieswiththenumberoftreesintheRFandthesizeofthetrainingsamplesetintheRF.Accordingtothelaws,researcherscanfindouttheoptimizedparametersintheRFalgorithminordertoachieve,asexpected,agoodresult.TheseresultsprovethatLIBSwiththeRFalgorithmcanexactlyclassifyAlalloyeffectively,preciselyandrapidlywithhighaccuracy,whichobviouslyhassignificantpracticalvalue.

简介：话题呈现前段时间发生了中美贸易战(tradewar),社会上开始出现一种不和谐的声音——“全民应该取消学英语”。为此,你们班也展开了激烈的讨论。假如你叫Amy,请你写一篇发言稿,旗帜鲜明地反对这种谬论,说明学习英语的重要性;再结合自身实际情况,谈一谈应如何学好英语。

简介：Theaccuracyoflaser-inducedbreakdownspectroscopy(LIBS)quantitativemethodisgreatlydependentontheamountofcertifiedstandardsamplesusedfortraining.However,inpracticalapplications,onlylimitedstandardsampleswithlabeledcertifiedconcentrationsareavailable.Anovelsemi-supervisedLIBSquantitativeanalysismethodisproposed,basedonco-trainingregressionmodelwithselectionofeffectiveunlabeledsamples.Themainideaoftheproposedmethodistoobtainbetterregressionperformancebyaddingeffectiveunlabeledsamplesinsemi-supervisedlearning.First,effectiveunlabeledsamplesareselectedaccordingtothetestingsamplesbyEuclideanmetric.Twooriginalregressionmodelsbasedonleastsquaressupportvectormachinewithdifferentparametersaretrainedbythelabeledsamplesseparately,andthentheeffectiveunlabeledsamplespredictedbythetwomodelsareusedtoenlargethetrainingdatasetbasedonlabelingconfidenceestimation.Thefinalpredictionsoftheproposedmethodonthetestingsampleswillbedeterminedbyweightedcombinationsofthepredictionsoftwoupdatedregressionmodels.Chromiumconcentrationanalysisexperimentsof23certifiedstandardhigh-alloysteelsampleswerecarriedout,inwhich5sampleswithlabeledconcentrationsand11unlabeledsampleswereusedtotraintheregressionmodelsandtheremaining7sampleswereusedfortesting.Withthenumbersofeffectiveunlabeledsamplesincreasing,therootmeansquareerroroftheproposedmethodwentdownfrom1.80%to0.84%andtherelativepredictionerrorwasreducedfrom9.15%to4.04%.

简介：Thetidalasymmetry-inducedsiltationbelowtidalbarriersisaworldwideproblemthatrestrictsregionalsocio-economicandenvironmentaldevelopment.Thehydrodynamicprocessesofthesmallmudestuaryalsofeatureahighuncertaintyafterestuaryrestorationmeasures.Inthisstudy,ahydrodynamicmodelbasedontheMIKE21isusedtoquantifytheresponsesoftidalasymmetrytoatwo-phaserestorationprojectinShuanglongEstuary,BohaiBay,China.Accordingtothenumericalmodelingresults,thetidalflatremovalintheupperestuary(first-phaserestoration)inducesthefloodasymmetryswitchingtotheebbasymmetryinunrestoredreachbutenhancesfloodasymmetryinwideningrestoredreach.Althoughthetidalasymmetryrevertstoflood-dominatedpatternafterfullrestorationovertheestuary,theimbalancebetweenfloodandebbvelocitiesisrelieved.Apossiblenetsedimenttransportpatternbasedonacomparisonofdominantasymmetriccurrentandactualsedimenttransportperiodshowsnetsedimentsintheupperestuaryandinlettransportseawardandlandward,respectively,inthefirst-phaserestoration,whereaslandwardnetsedimenttransportoccursinthewholeestuaryunderthesecond-phaserestorationscenario.Giventheseresults,weassumethataswitchfromtheflood-dominatedestuarytoebb-dominatedestuarycanbecausedbyredesigningthecross-sectionalprofile.ThequantitativecomparisonofLagrangianresidualcurrentsalsoimpliesthatachannel–shoalstructureratherthanaflatbathymetrycanpromotethemasstransport.Therefore,reshapingthechannel–tidalflatsysteminrestorationprojectscanpreventthesedimentationoftheestuaryandimprovethewaterenvironment.

简介：据说水利部将把是否利用Project软件进行项目管理,系统就按项目实际发生的数据进行整个项目计划的计算,阐明了Project软件在水利工程项目管理中的重要性

简介：Researchersoftensummarizetheirworkintheformofscientificposters.Postersprovideacoherentandefficientwaytoconveycoreideasexpressedinscientificpapers.Generatingagoodscientificposter,however,isacomplexandtime-consumingcognitivetask,sincesuchpostersneedtobereadable,informative,andvisuallyaesthetic.Inthispaper,forthefirsttime,westudythechallengingproblemoflearningtogeneratepostersfromscientificpapers.Tothisend,adata-drivenframework,whichutilizesgraphicalmodels,isproposed.Specifically,givencontenttodisplay,thekeyelementsofagoodposter,includingattributesofeachpanelandarrangementsofgraphicalelements,arelearnedandinferredfromdata.Duringtheinferencestage,themaximumaposterior(MAP)estimationframeworkisemployedtoincorporatesomedesignprinciples.Inordertobridgethegapbetweenpanelattributesandthecompositionwithineachpanel,wealsoproposearecursivepagesplittingalgorithmtogeneratethepanellayoutforaposter.Tolearnandvalidateourmodel,wecollectandreleaseanewbenchmarkdataset,calledNJU-FudanPaper-Posterdataset,whichconsistsofscientificpapersandcorrespondingposterswithexhaustivelylabelledpanelsandattributes.Qualitativeandquantitativeresultsindicatetheeffectivenessofourapproach.

简介：TransmissionofananisotropicmetasurfaceisanalyzedinapolarbaserelyingontheJonescalculus,andpolarizationconversionfromthespatialuniformpolarizationtothespatialnonuniformpolarizationisexplored.Simpleandcompactpolarizationconvertersbasedonrectangularholesorcrossholesetchedinsilverfilmaredesigned,andpolarizationconversionsfromthelinearandcircularpolarizationtotheradialandazimuthalpolarizationarerealized.Numericalsimulationsofthreedesignedpolarizationconvertersconsistingofrectangularholesequivalenttopolarizersandquarter-andhalf-waveplates,exhibittheperfectpolarizationconversion.Theexperimentresultsconsistentwiththesimulationsverifytheoreticpredictions.Thisstudyishelpfulfordesigningmetasurfacepolarizationconvertersandexpandingtheapplicationofametasurfaceinpolarizationmanipulations.

简介：Theprojectfocusesonthedynamicanalysisofconcretebeamsreinforcedwithsilica-nanoparticlesunderblastloading.Thestructureislocatedattwoboundaryconditions.TheeffectivecompositepropertiesareobtainedutilisingMori-Tanaktheory.Thestructureissimulatedwithsinusoidalsheardeformationtheory.Applyingstrainsandstress,thebeamenergiesarederivedandutilisingprincipalofHamilton,thefinalequationsarecalculated.Usingmethodofdifferentialquadrature(DQM)andNewmark,thestructuredynamicdeflectionisobtained.Theinfluencesofvolumefractionandagglomerationofsilicananoparticles,parametersofgeometrical,boundaryconditionandblastloadonthedynamicdeflectionarestudied.

简介：BaotouPulite's3,000-tonrareearthmetalsandalloysprojectstartedconstructionrecently.Theproject,withatotalinvestmentof100millionyuan,islocatedinBaotouJiuyuanIndustrialPark,coveringanareaof40mu.ItisexpectedtobeputintoproductioninSeptember2019.

简介：spatialsignatures.Theresultsoftransectanalysiswithlandscape-levelmetricsshowedthaturbanizationinthemetropolitanShanghairegionhasresultedindramaticincreasesinpatchdensity(PD),weattemptedtoquantifythespatialpatternofurbanizationintheShanghaimetropolitanarea.Theresultsoftransectanalysiswithclass-levelmetricsshowedthatthespatialpatternofurbanizationcouldbequantifiedreliablyusinglandscapemetricsanddifferentlandusetypesexhibiteddistinctive,patchdensityincreaseswhilepatchsizeandlandscapeconnectivitydecrease.However

简介：摘 要：简要说明 project软件的特点和应用，结合项目实际情况，就进度计划编制的一般流程和方法以及进度控制和偏差分析等方面进行阐述。

简介：Theultrafastfiberlaserhasattractedagreatdealofresearchinterestduetoitslowcost,highefficiency,andsimplemaintenance.Opticalpassivedevicesarevitalpartsofafiberlaser.Inordertoobtainafiberlaserwithhighquality,opticalpassivedeviceswithhighperformancearerequired.Here,wedemonstrateahighlyintegratedopticaldevicewiththecombinationofasaturableabsorber(SA),coupler,isolator,wavelengthdivisionmultiplexer,anderbium-dopedfiber.Thebuilt-inSAhasamodulationdepthof7%andcanwithstandhighpumppowerduetotheuniquestructureoftheproposeddevice.Theproposeddeviceisappliedtoanultracompactfiberlaser,whichgreatlysimplifiesthelaserstructureandreducesthesizeoftheproposedlaser.Thecentralwavelength,pulseduration,repetitionrate,andsignal-to-noiseratiooftheoutputsolitonare1560nm,1.06ps,25.8MHz,and50dB,respectively.Theproposeddevicehasgreatpotentialforapplicationinhigh-powerandhigh-frequencyfiberlasers.Theproposedultracompactfiberlaserhasimportantapplicationsinopticalcommunication,opticalsensing,opticalfrequencycombs,andmicromachining.

简介：VesselMonitoringSystem(VMS)providesanewopportunityforquantifiedfishingresearch.ManyapproacheshavebeenproposedtorecognizefishingactivitieswithVMStrajectoriesbasedonthetypesoffishingvessels.However,oneresearchproblemisstillcallingforsolutions,howtoidentifythefishingvesseltypebasedononlyVMStrajectories.ThisproblemisimportantbecauseitrequiresthefishingvesseltypeasapreliminarytorecognizefishingactivitiesfromVMStrajectories.Thispaperproposesfishingvesseltypeidentificationscheme(FVID)basedonlyonVMStrajectories.FVIDexploitsfeatureengineeringandmachinelearningschemesofXGBoostasitstwokeyblocksandclassifiesfishingvesselsintoninetypes.ThedatasetcontainsallthefishingvesseltrajectoriesintheEastChinaSeainMarch2017,including10031pre-registeredfishingvesselsand1350unregisteredvesselsofunknowntypes.Inordertoverifytypeidentificationaccuracy,wefirstconducta4-foldcross-validationonthetrajectoriesofregisteredfishingvessels.Theclassificationaccuracyis95.42%.WethenapplyFVIDtotheunregisteredfishingvesselstoidentifytheirtypes.Afterclassifyingtheunregisteredfishingvesseltypes,theirfishingactivitiesarefurtherrecognizedbasedupontheirtypes.Atlast,wecalculateandcomparethefishingdensitydistributionintheEastChinaSeabeforeandafterapplyingtheunregisteredfishingvessels,confirmingtheimportanceoftypeidentificationofunregisteredfishingvessels.

简介：AIM:Toinvestigateandcomparetheefficacyoftwomachine-learningtechnologieswithdeep-learning(DL)andsupportvectormachine(SVM)forthedetectionofbranchretinalveinocclusion(BRVO)usingultrawide-fieldfundusimages.METHODS:Thisstudyincluded237imagesfrom236patientswithBRVOwithamean±standarddeviationofage66.3±10.6yand229imagesfrom176non-BRVOhealthysubjectswithameanageof64.9±9.4y.Trainingwasconductedusingadeepconvolutionalneuralnetworkusingultrawide-fieldfundusimagestoconstructtheDLmodel.Thesensitivity,specificity,positivepredictivevalue(PPV),negativepredictivevalue(NPV)andareaunderthecurve(AUC)werecalculatedtocomparethediagnosticabilitiesoftheDLandSVMmodels.RESULTS:FortheDLmodel,thesensitivity,specificity,PPV,NPVandAUCfordiagnosingBRVOwas94.0%(95%CI:93.8%-98.8%),97.0%(95%CI:89.7%-96.4%),96.5%(95%CI:94.3%-98.7%),93.2%(95%CI:90.5%-96.0%)and0.976(95%CI:0.960-0.993),respectively.Incontrast,fortheSVMmodel,thesevalueswere80.5%(95%CI:77.8%-87.9%),84.3%(95%CI:75.8%-86.1%),83.5%(95%CI:78.4%-88.6%),75.2%(95%CI:72.1%-78.3%)and0.857(95%CI:0.811-0.903),respectively.TheDLmodeloutperformedtheSVMmodelinalltheaforementionedparameters(P<0.001).CONCLUSION:TheseresultsindicatethatthecombinationoftheDLmodelandultrawide-fieldfundusophthalmoscopymaydistinguishbetweenhealthyandBRVOeyeswithahighlevelofaccuracy.TheproposedcombinationmaybeusedforautomaticallydiagnosingBRVOinpatientsresidinginremoteareaslackingaccesstoanophthalmicmedicalcenter.