简介:1IntroductionandPreliminariesItiswellknownthatthesmallestsingularvalueofamatrixisimportantinmatrixthoeryandapplications.VarahandQi,Johnsongavesomeresultsonlowerboundofthesmallestsingularvalue[1—4].Here,weusetheblockpartitionedformofamatrixtoob-taingeneralizedlowerboundsforσn(A).Furthermore,byscalingwithamatrixDweob-tainanewlowerboundforσAofmatriceswhicharenotpositivedefinitematrices.
简介:§1.IntroductionItisveryimportanttostudythetheoryofthemultidimensionalhyperbolicconservationlaws.Atypicalmodelisthefollowinginitialvalueproblem
简介:Intheframeworkofgameswithcoalitionstructure,weintroduceprobabilisticOwenvaluewhichisanextensionoftheOwenvalueandprobabilisticShapleyvaluebyconsideringthesituationthatnotallprioriunionsareabletocooperatewithothers.Thenweusefiveaxiomsofprobabilisticefficiency,symmetricwithincoalitions,symmetricacrosscoalitionsapplyingtounanimitygames,strongmonotonepropertyandlinearitytoaxiomatizethevalue.
简介:Basedonthegeneralsolutionofthree-dimensionalproblemsinpiezoelectricmedium,withthemethodofGreen’sfunctins,axisymmetricboundary-valueproblemsarediscussed.Thepurposeofthisresearchisforanalyzingtheeffectiveonmechanicsandelectricityofthepiezoelectricceramicscausedbyvoidsandinclusions.Thedisplacement,tractionandelectricGreen’sfunctionscorrespondingtocircularringloadsactingintheinteriorofapiezoelectricceramicareobtained.AcylindricalcoordinatesystemisemployedandHankeltransformareappliedwithrespecttoradialcoor-dinates.ExplicitsolutionsforGreen’sfunctionsarepresentedintermsofinfiniteintegralsofLipshitz-Hankeltype.Bysolvingatractionboundary-valueproblem,thesolutionschemeisillustrated.
简介:Anewapproachbasedonmultiwaveletstransformationandsingularvaluedecomposition(SVD)isproposedfortheclassificationofimagetextures.LowersingularvaluesaretruncatedbasedonitsenergydistributiontoclassifythetexturesinthepresenceofadditivewhiteGaussiannoise(AWGN).Theproposedapproachextractsfeaturessuchasenergy,entropy,localhomogeneityandmax-minratiofromtheselectedsingularvaluesofmultiwaveletstransformationcoefficientsofimagetextures.Theclassificationwascarriedoutusingprobabilisticneuralnetwork(PNN).Performanceoftheproposedapproachwascomparedwithconventionalwaveletdomaingraylevelco-occurrencematrix(GLCM)basedfeatures,discretemultiwaveletstransformationenergybasedapproach,andHMMbasedapproach.Experimentalresultsshowedthesuperiorityoftheproposedalgorithmswhencomparedwithexistingalgorithms.