國際會議英文演講稿

國際會議英文演講稿篇一：國際會議作報告英語發(fā)言稿Thankyou,prof.???.Mynameis???..I’mfrom???..Iamverypleasedtobeheretojointhisforum.Thetopicofmypresentationispropertiesofrapidconstructionmaterialsforsoilpavementoffieldairfield.Asisshowninthepicture,themainpartsofmyresearchareaboutsoilpavement.Mypresentationwillincludethesefourparts:First,somebackgroundinformationaboutthisresearch;second,themainworkwehavedone;third,someconclusionswehavegotandthelast:innovationandpresentationofourpublishedpapers.WhyIchoosethisitemIthinkitcanbeillustratedfromthefollowingfourparts.First,theexistingquantityofairfieldsisstillnotsufficientandtheairfieldshavemanyshortcomingsespeciallyinwartime.Second,thecomplementaryfacilities,suchashighwayrunwaysarefarlessthanairfields,however,havemoreweakness.Third,acertainamountoffieldairfieldisquitenecessaryconsideringsomeemergenciessuchasrescueanddisasterrelief.Forth,thefieldairfieldcanfillthevoidofairfieldandtheycanbecombinedtobeairfieldnetwork.Themeaningandaimofthisresearchcontainsthreeparts.Fast,convenientandvalidity,fastmeansthefieldairfieldmustbeconstructedasfastaspossible,convenientmeanstheconstructionshouldneedtheminimumequipment,laborandmaterialsconsideringtheactualconstructioncondition,validitymeanstheconstructedairfieldisabletosupporttheoperationofgivenaircraftinspecificallytime.Justlikemanyotherterritories,thesituationoftheresearchisthattheArmytakesadvancedline.TheArmydeclaresthattheycanreachtoanywhereontheearthin96hours,themostimportantmethodforforceprojectionisthoughaircraft,thusrapidconstructionofpavementisthekeyproblemforrapidforcetransportation.Themainworkwehavedonecanbesummarizedasfourparts,materialschoosing,schememaking,mechanicalpropertiesresearchandwater-stablepropertiesresearch.Wechoosetwokindsofsoils,whicharegotfromXi’an,ShanxiprovinceandJiuquan,Gansuprovinceseparately.ThesandfromBaRiverwasconsideratetoinvestigatetheinfluenceofsandtothepropertiesofstabilizedsoil.Thechosenthreekindsofpowdersarecement,limeandnew-typestabilizerdevelopedbyChang'anUniversity.Theprinciplesinconsideringthefunctionof4kingsoffibersarereferringdifferentlength,typeandmixingthem.Onaccountofthetime,Iwillmakeabriefdescriptionabouttheexperimentscheme.Insummary,threepartswereproposedtodistinguishtheaffectingfactorsinmakingexperimentscheme.Theyarepowdercontrol,fibercontrolandotherfactors.Takingpowdercontrolforexample,thedosageofcementisrespectively6%,8%and10%whenthesoilisstabilizedonlybycement,whilethedosageofcementdecreaseto3%,5%and7%whenthelimeisaddictedtostabilizedsoil.Thefollowingtwofactorsarestabilizerandsand.Sixkindsofexperimentswereperformedtoinvestigatetheinfluenceofabovefactorstothemechanicalpropertiesofstabilizedsoil.Theaimofcompactiontestistofindthemaximumdrydensityandoptimummoisturecontent.Theaimofcompressionstrengthtestistodeterminetheoptimumdosageofcement,lime,powderstabilizerandfiber,meanwhileevaluatingtheperformanceofstabilizedsoil.Theaimofsplittingtensionstrengthtestissimilartocompressionstrengthtest,theleftpictureissamplestabilizedbycement,whiletherightpictureisthesamplestabilizedbyfiberandcement.Thedirectsheerisanotherimportantparameteringeotechnicalengineering.Itinfluencesthefoundationbearingcapacityandmanyotherpropertiesespeciallyforsoilbaseandbasecourse.Theleftpictureshowsthecourseofmakingsampleandtherightpictureshowsthetestprocess.TheCBRtestandreboundmodulustestarereferencedfromhighwaytestspecificationtoevaluatingthecomprehensivecapacitiesofeachstructurelevelofthepavement.Forboththetwotests,theleftpictureshowsthecourseofmakingsampleandtherightpictureshowsthetestprocess.Whatshouldbenotedisthatthenumberofsampleisatleast6,thelastresultistheaveragevalueofthesedategotfromtestaftereliminatingthebadresults.Fourkindsofexperimentswereperformedtoinvestigatetheinfluenceofabovefactorstothewater-stablepropertiesofstabilizedsoil.Thescouringtestisnotthestatedexperimentincurrentspecification.Itisperformedbyusthroughlookinguplargequantityofinterrelatedliterature,andtwodifferentwaystocarryout.Theleftpictureshowsthemethodofvibrationtableandtherightpictureshowsthemethodoffatiguetestinstrument.Penetranttestreferstotheexperimentinrelatingconcretespecification.Theleftpictureshowstheprocessofsaturation,therightpictureshowsthetestprocess.Cantabriatestandothertestsarealloriginalexperiments;theyareusedinstabilizedsoilforfirsttime,hereIwillnotdevelopmynarrative.Asregardstheinnovation,Ithinkitthroughoutthewholeresearch,includingmaterialschoosing,schememaking,mechanicalandwater-stableexperiments.Ithinkitcanbedrawledfromthefollowingkeywords,suchassoilchoosing,sand,powders,fibers,andsoon.Threemainpartscanbesummarized.First,selectingtwokindsofsoils,threekindsofpowders,severalcombinations;second,severalkindsoffibers,differentlengthandadmixture;third,comprehensiveexperiments,testmethodandtestinstrument.篇二：英文國際學術會議開幕詞演講稿InternationalConferenceonSpaceTechnologyDistinguishedguests,distinguisheddelegates,ladiesandgentlemen,andallthefriends:AtthisspecialtimeofwonderfulSeptember,inthisgrandhallofthebeautifulcity,ourrespectableguestsareheregettingtogether.JointlysponsoredbytheInternationalAstronauticalFederation(IAF),InternationalAcademyofAstronautics(IAA)andtheInternationalInstituteofSpaceLaw(IISL),undertakenbyChinaNationalConventionCenteratBeijing,thesixty-fourthsessionoftheInternationalAstronauticalCongresswillbeopen.Now,firstofall,pleaseallowmetogiveourheartywelcometoallofyoupresent,andthankyou,foryourfriendlycoming.Wefeelsoproud,andappreciatedaswelltobethehostoftheevent.Forthisconference,wearefollowingtheagendahere.Themeetingissupposedtolastforfivedaysitisthefirstcongresswhichcoversthetruesenseofspacescienceandexploration,spaceapplicationsandoperations,spaceinfrastructure,spaceandsocietymultidimensionalfields.Andittobeseparatedintotwoparts,tobeginwith,we’llinvitesomerepresentativesfromourgueststogivelecturesabouttheirlatestresearchesandreportsontheissue,andthenwewillhavesomefinallyIwishyouanunforgettableandprefectexperiencehere.Thankyou!篇三：英文國際會議講稿PPT(1)大家上午好！今天我匯報的主題是：基于改進型LBP算法的運動目標檢測系統(tǒng)。運動目標檢測技術能降低視頻監(jiān)控的人力成本，提高監(jiān)控效率，同時也是運動目標提取、跟蹤及識別算法的基礎。圖像信號具有數(shù)據(jù)量大，實時性要求高等特征。隨著算法的復雜度和圖像清晰度的提高，需要的處理速度也越來越高。幸運的是，圖像處理的固有特性是并行的，尤其是低層和中間層算法。這一特性使這些算法，比較容易在FPGA等并行運算器件上實現(xiàn)，今天匯報的主題就是關于改進型LBP算法在硬件上的實現(xiàn)。goodmorningeveryone.MyreportisaboutaMotionDetectionSystemBasedonImprovedLBPOperator.Automaticmotiondetectioncanreducethehumancostofvideosurveillanceandimproveefficiency['f()ns]，itisalsothefundamentofobjectextraction,trackingandrecognition[rekg'n()n].Inthiswork,efforts['efts]weremadetoestablishthebackgroundmodelwhichisresistancetothevariationofillumination.AndourvideosurveillancesystemwasrealizedonaFPGAbasedplatform.PPT(2)目前，常用的運動目標檢測算法有背景差分法、幀間差分法等。幀間差分法的基本原理是將相鄰兩幀圖像的對應像素點的灰度值進行減法運算，若得到的差值的絕對值大于閾值，則將該點判定為運動點。但是幀間差分檢測的結果往往是運動物體的輪廓，無法獲得目標的完整形態(tài)。Currently,OpticFlow,BackgroundSubtractionandInter-framedifferenceareregardasthethreemainstreamalgorithmstodetectmovingobject.Inter-framedifferencebasedmethodneednotmodel['mdl]thebackground.Itdetectsmovingobjectsbasedontheframedifferencebetweentwocontinuousframes.Themethodiseasytobeimplementedandcanrealizereal-timedetection,butitcannotextractthefullshapeofthemovingobjects[6].PPT(3)在攝像頭固定的情況下，背景差分法較為簡單，且易于實現(xiàn)。若背景已知，并能提供完整的特征數(shù)據(jù)，該方法能較準確地檢測出運動目標。但在實際的應用中，準確的背景模型很難建立。如果背景模型如果沒有很好地適應場景的變化，將大大影響目標檢測結果的準確性。像這副圖中，背景模型沒有及時更新，導致了檢測的錯誤。Thebasicprincipleofbackgroundremovalmethodisbuildingabackgroundmodelandprovidingaclassificationofthepixelsintoeitherforegroundorbackground[3-5].Inacomplexanddynamicenvironment,itisdifficulttobuildarobust[r()'bst]backgroundmodel.PPT(4)上述的幀間差分法和背景差分法都是基于灰度的?；诨叶鹊乃惴ㄔ诠庹諚l件改變的情況下，性能會大大地降低，甚至失去作用。Thealgorithmswehavediscussedaboveareallbasedongrayscale.Inpracticalapplicationsespeciallyoutdoorenvironment,thegrayscalesofeachpixelareunpredictablyshiftybecauseofthevariationsintheintensityandangleofillumination.PPT(5)為了解決光照改變帶來的基于灰度的算法失效的問題，我們考慮用紋理特征來檢測運動目標。而LBP算法是目前最常用的表征紋理特征的算法之一。首先在圖像中提取相鄰9個像素點的灰度值。然后對9個像素中除中心像素以外的其他8個像素做二值化處理。大于等于中心點像素的，標記為1，小于的則標記為0。最后將中心像素點周圍的標記值按統(tǒng)一的順序排列，得到LBP值，圖中計算出的LBP值為10001111。當某區(qū)域內(nèi)所有像素的灰度都同時增大或減小一定的數(shù)值時，該區(qū)域內(nèi)的LBP值是不會改變的，這就是LBP對灰度的平移不變特性。它能夠很好地解決灰度受光照影響的問題。Inordertosolvetheaboveproblems,weproposedanimprovedLBPalgorithmwhichisresistancetothevariationsofillumination.Localbinarypattern(LBP)iswidelyusedinmachinevisionapplicationssuchasfacedetection,facerecognitionandmovingobjectdetection[9-11].LBPrepresentsarelativelysimpleyetpowerfultexturedescriptorwhichcandescribetherelationshipofapixelwithitsimmediateneighborhood.ThefundamentalofLBPoperatorisshowedinFig1.ThebasicversionofLBPproduces256texturepatternsbasedona9pixelsneighborhood.Theneighboringpixelissetto1or0accordingtothegrayscalevalueofthepixelislargerthanthevalueofcentricpixelornot.Forexample,inFig17islargerthan6,sothepixelinfirstrowfirstcolumnissetto1.Arrangingthe8binarynumbersincertainorder,wegetan8bitsbinarynumber,whichistheLBPpatternweneed.Forexamplein,theLBPis10001111.LBPistolerant['tl()r()nt]againstilluminationchanging.Whenthegrayscalesofpixelsina9pixelswindowareshiftedduetoilluminationchanging,theLBPvaluewillkeepunchanged.PPT(6)圖中的一些常見的紋理，都能用一些簡單的LBP向量表示，對于每個像素快，只需要用一個8比特的LBP值來表示。Therearesometextures,andtheycanberepresentbysomesimple8bitLBPpatterns.PPT(7)從這幅圖也可以看出，雖然灰度發(fā)生了很大的變化，但是紋理特征并沒有改變，LBP值也沒有變化。Youcansee,inthesepicture,althoughthegrayscalechangealot,buttheLBPpatternskeepitvalue.PPT(8)上述的算法是LBP算法的基本形式，但是這種基本算法不適合直接應用在視頻監(jiān)控系統(tǒng)中。主要有兩個原因：第一，在常用的視頻監(jiān)控系統(tǒng)中，特別是在高清視頻監(jiān)控系統(tǒng)中，9個像素點覆蓋的區(qū)域很小，在如此小的區(qū)域內(nèi)，各個像素點的灰度值十分接近，甚至是相同的，紋理特征不明顯，無法在LBP值上體現(xiàn)。第二，由于以像素為單位計算LBP值，像素噪聲會造成LBP值的噪聲。這兩個原因?qū)е掠嬎愠龅腖BP值存在較大的隨機性，甚至在靜止的圖像中，相鄰兩幀對應位置的LBP值也可能存在差異，從而引起的誤檢測。為了得到更好的檢測性能，我們采用基于塊均值的LBP算法。這種方法的基本原理是先計算出3X3個像素組成的的像素塊的灰度均值，以灰度均值作為該像素塊的灰度值。然后以3X3個像素塊(即9X9個像素)為單位，計算LBP值。ThetypicalLBPcannotmeettheneedofpracticalapplicationofvideosurveillancefortworeasons:Firstly,a“window”whichonlycontains9pixelsisasmallareainwhichthegrayscalesofpixelsaresimilarorsametoeachother,andthetexturefeatureinsuchasmallareaistooweaktobereflectedbyaLBP.Secondly,pixelnoisewillimmediatelycausethenoiseofLBP,whichmayleadtoalargenumberofwrongdetection.Inordertoobtainabetterperformance,weproposedanimprovedLBPbasedonthemeanvalueof“block”.Inouralgorithm,oneblockcontains9pixels.ComparedwithoriginalLBPpatterncalculatedinalocal9neighborhoodbetweenpixels,theimprovedLBPoperatorisdefinedbycomparingthemeangrayscalevalueofcentralblockwiththoseofitsneighborhoodblocks(see).Byreplacingthegrayscalesofpixelswiththemeanvalueofblocks,theeffectofthepixelnoiseisreduced.ThetexturefeatureinsuchabiggerareaismoresignificanttobedescribedbyLBPpattern.PPT(9)運用LBP描述背景，其本質(zhì)上也是背景差分法的一種。背景差分法應用在復雜的視頻監(jiān)控場景中時，要解決建立健壯的背景模型的問題。駛入并停泊在監(jiān)控畫面中的汽車，被搬移出監(jiān)控畫面的箱子等，都會造成背景的改變。而正確的背景模型是正確檢測出運動目標并提取完整目標輪廓的基礎。如果系統(tǒng)能定時更新背景模型，將已經(jīng)移動出監(jiān)控畫面的物體“剔除”出背景模型，將進入監(jiān)控畫面并且穩(wěn)定停留在畫面中的物體“添加”入背景模型，會減少很多由于背景改變而造成的誤檢測。根據(jù)前一節(jié)的介紹，幀間差分法雖然無法提取完整的運動目標，但是它是一種不依賴背景模型就能進行運動目標檢測的算法。因此，可以利用幀間差分法作為當前監(jiān)控畫面中是否有運動目標的依據(jù)。如果畫面中沒有運動目標，就定期對背景模型進行更新。如果畫面中有運動目標，就推遲更新背景模型。這樣就能避免把運動目標錯誤地“添加”到背景模型中。 Inpracticalapplication,thebackgroundischangingrandomly.Fortraditionalbackgroundsubtractionalgorithmtheincapabilityofupdatingbackgroundtimelywillcausewrongdetection.Inordertosolvethisproblem,weproposeanalgorithmwithdynamicselfupdatingbackgroundmodel.Asweknow,Inter-framedifferencemethodcandetectmovingobjectwithoutabackgroundmodel,butthismethodcannotextractthefullshape.Backgroundsubtractionmethodcanextractthefullshapebutneedsabackgroundmodel.Thebasicprincipleofouralgorithmisrunningaframedifferencemovingobjectdetectionprocessconcurrently[kn'krntli]withthebackgroundsubtractionprocess.What’stimetoupdatethebackgroundisaccordingtotheresultofframedifferencedetection.PPT(10)運動目標檢測系統(tǒng)特別是嵌入式運動目標檢測系統(tǒng)在實際應用中要解決實時性的問題。比如每秒60幀的1024X768的圖像，對每個像素都運用求均值，求LBP等算法，那么它的運算量是十分巨大的，為此我們考慮在FPGA上用硬件的方式實現(xiàn)。IfLBPalgorithmisimplementedinasoftwareway,itwillbeveryslow.FPGAhavefeaturesofconcurrentcomputation,reconfigurationandlargedatathroughput.Itissuitabletobebuiltanembeddedsurveillancesystem.ThealgorithmintroducedaboveisimplementedonaFPGAboard.PPT(11)這就是我們硬件實現(xiàn)的系統(tǒng)結構圖。首先輸入系統(tǒng)的RGB像素信號的濾波、灰度計算及LBP計算，得到各個像素塊的LBP值。然后背景更新控制模塊利用幀差模塊的檢測結果控制背景緩存的更新。區(qū)域判定模塊根據(jù)背景差模塊的輸出結果，結合像素塊的坐標信息，對前景像素塊進行區(qū)域判定。Thestructureofthesystemisshowedinthisfigure.Inthissystem,aVGAsignalisinputtothedevelopmentboard.andtheLBPpatterniscalculated,Framedifferencemodulealsocomparesthecurrentframeandthepreviousframetodeterminewhetherthereisamovingobjectinthesurveillancevision.Ifthesurveillancevisionisstaticforacertainamountofframe,thebackgroundmodelwillbeupdated.PPT(12)圖中是LBP計算模塊。圖中所示的窗口提取結構可以實現(xiàn)3X3像素塊窗口的提取。像素信號按順序輸入該結構，窗口中的數(shù)據(jù)就會按順序出現(xiàn)在Pixell-Pixel9這9個寄存器中，從而在最短的延時內(nèi)提取出相鄰9個像素點的灰度值。行緩存的大小等于每一行圖像包含的像素個數(shù)減1。將9個像素點的灰度值通過求均值模塊，可以求出一個像素塊的像素均值。將像素塊均值作為輸入再次通過類似的結構，可以提取出3X3個相鄰像素塊的灰度值。這時行緩存的大小為每一行包含的像素塊的個數(shù)減1。再用9個窗口的灰度值作為輸入，用比較器陣列計算出最終的LBP值。ToachieverealtimecomputationoftheLBP,acircuitstructureisputforwardasshowedinTwolinebuffersandnineresistersareconnectedinthewayshowedinthefigure.Nineneighborpixelsareextractedwithminimum['mnmm]delay,andthemeanvalueofthisblockiscalculatedbythemeanvaluecalculatemodulewhichcontainssomeaddersandshifters.Themeanvaluesoftheblocksareinputtedtoasimilarstructureandextractedinasimilarway,andtheLBPiscalculatedbytheconsequenceLBPcalculatemodule.PPT(13)求均值模塊采用如圖3-12所示的四級流水方式實現(xiàn)。在算法的設計過程中，需要求出的是3X3像素塊中9個像素的均值。但是在硬件實現(xiàn)時，為了更合理地利用硬件資源，只計算剔除中心像素后的8個像素的均值。這樣做可以在不對計算結果造成太大影響的情況下減少加法器的使用。而且在求均值的最后一級流水，除8運算比除9運算更容易實現(xiàn)。因為8是2的整數(shù)冪，除8運算只需要將各個像素的和右移3位。而除9運算在FPGA中需要專用的DSP模塊來完成。PPT(14)如圖所示，塊均值計算模塊計算出的8個塊均值被圖3-11中的窗口提取模塊提取出來，并作為比較器陣列的輸入，比較器的輸出結果用0和1表示。最終的比較結果按一定的順序排列，重新拼接成一個8位的二進制數(shù)，即LBP值。LBP計算電路沒有采用流水結構，在一個時鐘周期內(nèi)就能得到計算結果。PPT(15)這個是在系統(tǒng)測試中，實現(xiàn)對多個目標的檢測。Inthissystemtest,weachieveamulti-objectdetection.PPT(16)這個圖是對動態(tài)背景更新的測試，在監(jiān)控區(qū)域中劃定一個目標區(qū)域，把一個靜止的物體放置到目標區(qū)域中。在前3分鐘內(nèi)，系統(tǒng)會將其當做前景目標，矩形窗口會以閃爍的形式發(fā)出報警信號。3分鐘過后，由于物體一直處于靜止狀態(tài)，系統(tǒng)檢測到了10800個靜止幀，于是更新背景模型。靜止的物體被當做背景的一部分，此后窗口不再閃爍。經(jīng)驗證，該系統(tǒng)能夠正確實現(xiàn)背景模型更新算法。Thisisthetestfortheautobackgroundupdate.Weputastaticsobjectinthesurveillancearea，atthebeginningthisistrustedasamovingobject.after3minutes,thesystemreceivetenthousandstaticframes,andthenupdatethebackgroundmodel.Thenthisobjectisregardasapartofthebackground.PPT(17)此外為了驗證系統(tǒng)對室外光照變化抑制能力，我們選取了大量有光照變化，并且有運動目標的視頻對系統(tǒng)進行了測試。Inordertoverifytheresistancetothevarationofillumination,acertificationexperimentisdesigned,andtheROCcurvesofthetwoalgorithmsbasedonLBPandgrayscaleareplottedandcompared.Anumberofshortvideoclipswithshiftyandfixedillumination,includingpositivesampleswithmovingobjectsandnegativesampleswithoutmovingobjects.PPT(18)測試平臺如圖所示。用一臺PC機作為測試信號的輸出源，然后在PC機中播放視頻，并將視頻VGA信號發(fā)送給運動目標檢測系統(tǒng)，模擬真實的監(jiān)控環(huán)境。FPGA將輸入信號和區(qū)域邊框圖形相疊加后在LCD上顯示。Thepictureofthecertificationexperimentisshowedinthispicture.APCactsasthesourceofthetestsignalwhichisinputtotheFPGAintheformofVGA.PassingthroughtheFPGAboard,videosignalisdisplayedonaLCDscreen.PPT(19)并最終描繪了系統(tǒng)的ROC特性曲線。在沒有光照強度變化的情況下，采用基于灰度的運動目標檢測算法的性能略優(yōu)于基于LBP值的運動目標檢測算法，兩種算法都能取得較好的檢測效果。但是在圖5-15中(測試集2)，也就是在光照強度變化的情況下，畫面整體灰度發(fā)生較大的改變，基于灰度的檢測算法的性能大幅度下降，接近于失效。而采用LBP值的檢測算法卻能維持較好的性能?？梢娀贚BP的檢測算法對抑制光照強度變化造成的誤檢測有較好的效果。ThistwofigurearetheROCcurvesoftheexperimentsusingouralgorithmandtraditionalgrayscale-basedalgorithm.Wecanseeinthewhichcorrespondstotheconditionwithfixedillumination,theperformanceofthegrayscale-basedalgorithmisslightlybetterthantheseofLBP-basedalgorithm,theycanbothdetectmovingobjecteffectively.Butinwhichcorrespondstotheconditionwithshiftyillumination,grayscalebasedalgorithmdeterioratesdrasticallyandnearlyloseefficacy['efks].ButtheimprovedLBPalgorithmstillkeepsagoodperformance.PPT(20)謝謝大家！Thanksforyourattention篇四：英文國際學術會議開幕詞演講稿InternationalConferenceonRemoteSensingTechnologyDistinguishedguests,distinguisheddelegates,ladiesandgentlemen,andallthefriends:AtthisspecialtimeofwonderfulMarch,inthisgrandhallofthebeautifulcampus,Ourrespectableguestsareheregettingtogether.JointlysponsoredbyChinaRemoteSensingAssociation,undertakenbyRemoteSensingInstitutionofNUISTatNanjing,thefirstInternationalConferenceonRemoteSensingtechnology,willbeopen.Now,Firstofall,pleaseallowmetogiveourheartywelcometoallofyoupresent,andthankyou,foryourfriendlycoming.Wefeelsoproud,andappreciatedaswelltobethehostoftheevent.Itisagreathonorforustohaveallyouheretoattendthisconference,ofwhichthethemeistheacademicexchangeabouttheadvancedtechnologiesonRS.HereI'dbedelightedtointroduceourconventioneersinbrief.Apartfromourfacultyandstudents,Mostofthedelegatesandguestsareprestigiousexpertsandscientists,whoarerelatedinthesefieldsfromallovertheworld.Withmanysignificantachievements,theyarethemostdynamicleadersinthemovementsofthescienceandtechnology.Asthehost,Iwouldliketotakethisopportunitytogiveyouageneralintroductionaboutourschool.NanjingUniversityofInformationScience&Technology(NUIST),foundedin1960andrenamedfromNanjingInstituteofMeteorologyinXX,wasdesignatedin1978asoneofthekeyinstitutionsofhigherlearninginChina.Theuniversityconsistsof24departmentsorcolleges,12scientificresearchinstitutionsandoneinternationaltrainingcenter.Theuniversity,coveringanareaof140hectareswithafloorspaceof4XX0squaremeters,boasts42basicandspeciallaboratoriessuchasKeyLaboratoryofMeteorologicalDisastersandSino-AmericanRemoteSensingLaboratory.Withatotalcollectionofover1,170,000books,thelibrarywaslistedasoneofthemostcompletedliteraturelibrariesinChinaintermsofatmosphericsciences.Forthisconference,wearefollowingtheagendahere.Themeetingissupposedtolastforthreedaysandtobeseparatedintotwoparts.Tobeginwith,we’llinvitesomerepresentativesfromourgueststogivelecturesabouttheirlatestresearchesandreportsontheissue,andthenwewillhavesomesymposiums.Duringtheconferencewearepleasedtobeyourguidetothiscity.Ifanythingneeded,don'thesitatetocontactus.Webelievebyourcollaborationwearesuretomakethisgatheringaconsummation.AndfinallyIwishyouanunforgettableandprefectexperiencehere.Thanks!篇五：模擬國際會議演講稿Recsplorer:RecommendationAlgorithmsBasedonPrecedenceMiningIntroductionThankyouverymuch,Dr.Li,foryourkindintroduction.Ladiesandgentlemen,Goodmorning!Iamhonoredtohavebeeninvitedtospeakatthisconference.BeforeIstartmyspeech,letmeaskaquestion.DoyouthinkrecomemdationsfromothersareusefulforyourinternetshoppingThankyou.Itisobviousthatrecommendationsplayanimportantroleinourdailyconsumptiondecisions.Today,mytopicisaboutRecommendationAlgorithmsBasedonPrecedenceMining.Iwanttoshareourinterestingresearchresultonrecommendationalgorithmswithyou.Thecontentofthispresentationisdividedinto5parts:insession1,Iwillintruducethetradictionalrecommendationandournewstrategy;insession2,IwillgivetheformaldefinitionofPrecedenceMining;insession3,Iwilltalkaboutthenovelrecommendationalgorithms;experimentalresultwillbeshowedinsession4;andfinally,Iwillmakeaconclusion.BodySession1:IntroductionThepictureonthisslideisaninstanceofrecommemdationapplicationonamazon.Recommendersystemsprovideadviceonproducts,movies,webpages,andmanyothertopics,andhavebecomepopularinmanysites,suchasAmazon.Manysystemsusecollaborativefilteringmethods.ThemainprocessofCFisorganizedasfollow:first,identifyuserssimilartotargetuser;second,recommenditemsbasedonthesimilarusers.Unfortunately,theorderofconsumeditemsisneglect.Inourpaper,weconsideranewrecommendationstrategybasedonprecedencepatterns.Thesepatternsmayencompassuserpreferences,encodesomelogicalorderofoptionsandcapturehowinterestsevolve.Precedenceminingmodelestimatetheprobabilityofuserfutureconsumptionbasedonpastbehavior.Andtheseprobabilitiesareusedtomakerecommendations.Throughourexperiment,precedenceminingcansignificantlyimproverecommendationperformance.Futhermore,itdoesnotsufferfromthesparsityofratingsproblemandexploitpatternsacrossallusers,notjustsimilarusers.Thisslidedemonstratesthedifferencesbetweencollaborativefilteringandprecedencemining.Supposethatthescenarioisaboutcourseselection.Eachquarter/semesterastudentchoosesacourse,andratesitfrom1to5.Figurea)showsfivetranscripts,atranscriptmeansalistofcourse.Uisourtargetstudentwhoneedrecommendations.Figureb)illustrateshowCFwork.Assumesimilarusersshareatleasttwocommoncoursesandhavesimilarrating,thenu3andu4aresimilartou,andtheircommoncoursehwillbearecommendationtou.Figurec)presentshowprecedenceminingwork.Forthisexample,weconsiderpatternswhereonecoursefollowsanother.Supposepatternsoccouratleasttwotranscripsarerecognizedassignificant,then(a,d),(e,f)and(g,h)arefoundout.Andd,h,andfarerecommendationtouwhohastakena,gande.NowIwillaprobabilisticframeworktosolvetheprecedenceminingproblems.Ourtargetuserhasselectedcoursea,wewanttocomputetheprobabilitycoursexwillfollow,,Pr[x|a].—ihowerve,whatwereallyneedtocalculateisPr[x|aX]ratherthanPr[x|a].Becauseinourcontext,wearedecidingifxisagoodrecommendationforthetargetuserthathastakena.Thusweknowthatourtargetuser'stranscriptdoesnothavexbeforea.Forinstance,thetranscriptno.5willbeomitted.Inmorecommonsituation,ourtargetuserhastakenalistofcourses,T={a,b,c,…}not—■justa.Thus,whatreallyneedisPr[x|TX].Thequestionishowtofigureoutthisprobability.Iwillansweritlater.Session2:PrecedenceMiningWeconsiderasetDofdistinctcourses.Weuselowercaseletters(,a,b,???)torefertocoursesinD.AtranscriptTisasequenceofcourses,,a->b->c->d.ThenthedefinitionofTop-kRecommendationProblemisasfollows.GivenasettranscriptsoverDfornusers,theextratranscriptTofatargetuser,andadesirednumberofrecommendationsk,ourgoalisto:Assignascorescore(x)(between0and1)toeverycoursexEDthatreflectshowlikelyitisthetargetstudentwillbeinterestedintakingx.IfET,thenscore(x)=0.Usingthescorefunction,selectthetopkcoursestorecommendtothetargetuser.Tocomputescores,weproposetousethefollowingstatistics,wherex,yED:f(x):thenumberoftranscriptsthatcontainx.g(x;y):thenumberoftranscriptsinwhichxprecedescoursey.Thisslideshowsthecalculationresultoff(x)andg(x,y).Forexample,fromthetable,weknowthatf(a)is10andg(a,c)is3.WeproposeaprecedenceminingmodeltosolvetheTop-kRecommendationProblem.Hereare—■somenotation:xy,whichwehavememtionedinsession1,referstotranscriptwherexoccurswithoutaprecedingy;x—yreferstotranscriptwherexoccurswithoutyfollowingit.Weusequantitiesf(x)andg(x,y)tocompteprobabilitiesthatencodetheprecedenceinformation.Forinstance,fromformular1to7.Iwouldnottellthedetailofallformulars.Wejustpayattentionto—formular5,notethatthisquantityaboveisthesameas:Pr[x—y|yx]whichwillbeusedtocomputescore(x).Asweknow,thetargetuserusuallyhastakenalistofcoursesratherthanacourse,soweneedto—■extentourprobabilitycalculationformulars.Forexample,supposeT={a,b},Pr[xT]theprobabilityxoccurswithouteitheranaorbprecedingit;Pr[x—T]theprobabilityxoccurswithouteitheranaorbfollowingit.Thisprobabilitycanbecalculatedexactly.SohowtocalculateitSession3:RecommendationAlgorithmsLet'sreviewsession2.Themaingoaloftherecommendationalgorithmsistocalculatethescore(x),andthenselectthetopkcoursesbasedonthesescores.TraditionalrecommendationalgorithmscomputearecommendationscoreforacoursexinDonlybasedonitsfrequencyofoccurence.Itdoesnottakeintoaccountthecoursestakenbythetargetuser.OurrecommendationalgorithmscalledSingleMCconquertheshortcomingofthetraditionalones.Itcomputesthescore(x)usingtheformular5.Thedetailisasfollows:astudentwithatranscripToftakencourses,forthecoursey￡T,ifyandxappeartogetherintranscriptssatisfiestheyx],—threshold9,thencomputethePr[x—yreflectingthelikelihoodthestudentwilltakecourseyx],x—■andignoringtheeffectoftheothercoursesinT;finallythemaximumofPr[x—y|yx]ischoosenasthescore(x).Hereisthecalculationformularofscore(x)ofSignleMC.Forexample,withthehigerscore,dwillberecommended.AnothernewrecommendationalgorithmnamedJointProbabilitiesalgorithm,JointPforshort,isproposed.UnlikeSingleMC,Joi