简介：Securityofsmarttechnologieslikesmartgridsandcrowdenergysystemscannotrelyonlyontechnicalsolutions,humansplayasignificantroleinfailure,securityculture,informationsecurity,cybersecurity,trustandsharing,andperceptionsandconcern.Thispaperinvestigatessecurityissuesinsmarttechnologiesandtherelatedroleofhumanfailure,whetheritisintentionalorunintentional.Methodsthathelpreducingthisfailurearediscussed.Ahumanorientedframeworkforfailurereductionispresentedinordertoenhancesecurity.IndexTermsCrowdenergy,humanrole,intentionalfailure,security,smartgrids,unintentionalfailure.1.IntroductionManystateofthearttechnologiesaretaggedwiththe'smart'label.Ingeneralunderstandingthismeansinterconnectedness,interactivityand4.0automation,i.e.theapplicationofsensorsandinformationandcommunicationtechnology(SICT)andprocessinganever-changingandexpandingdata/knowledgebase.Fromahigherpointofview'smart'standsbutformorethanjusttheabove-mentionedtechnicalaspects;alsoandinparticularsocial,political,economicandecologicalaspectswillbeaddressed.Thismeansthat'smartmeetschallengestoimprovesustainablewelfare'[1].Inthissense,forexample,thetermsmartgridaddressesnotonlythecommunicativeinterconnectionandcontroloftheentitiesofapowersystem,i.e.technology,butpreciselyalsosustainability,conservationofresources,renewability,andefficiency.Securityhasbeenveryimportantfornewtechnologiesandsystemslikeelectricalgridsandpowerplants.Theseneedtobesecuredcarefullybecauseweaknessesinthesecuritymightleadtopartialdamageandreductionofserviceinseveralways.Inextremecasesdestructioncanhap-

简介：Thispaperpresentsahumandetectionsysteminavision-basedhospitalsurveillanceenvironment.Thesystemiscomposedofthreesubsystems,i.e.backgroundsegmentationsubsystem(BSS),humanfeatureextractionsubsystem(HFES),andhumanrecognitionsubsystem(HRS).ThecodebookbackgroundmodelisappliedintheBSS,thehistogramoforientedgradients(HOG)featuresareusedintheHFES,andthesupportvectormachine(SVM)classificationisemployedintheHRS.Bymeansoftheintegrationofthesesubsystems,thehumandetectioninavision-basedhospitalsurveillanceenvironmentisperformed.Experimentalresultsshowthattheproposedsystemcaneffectivelydetectmostofthepeopleinhospitalsurveillancevideosequences.

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简介：Thispaperpresentsahumanactionrecognitionmethod.Itanalyzesthespatio-temporalgridsalongthedensetrajectoriesandgeneratesthehistogramoforientedgradients(HOG)andhistogramofopticalflow(HOF)todescribetheappearanceandmotionofthehumanobject.Then,HOGcombinedwithHOFisconvertedtobag-of-words(BoWs)bythevocabularytree.Finally,itappliesrandomforesttorecognizethetypeofhumanaction.Intheexperiments,KTHdatabaseandURADLdatabasearetestedfortheperformanceevaluation.Comparingwiththeotherapproaches,weshowthatourapproachhasabetterperformancefortheactionvideoswithhighinter-classandlowinter-classvariabilities.IndexTermsBag-of-words(BoWs),densetrajectories,histogramofopticalflow(HOF),histogramoforientedgradient(HOG),randomforest,vocabularytree.

简介：Becauseofitspotentialapplicationsinagriculture,environmentmonitoringandsoon,wirelessundergroundsensornetwork(WUSN)hasbeenresearchedmoreandmoreextensivelyinrecentyears.ThemainandmostimportantdifferenceofWUSNtoterrestrialwirelesssensornetwork(WSN)isthechannelcharacteristics,whichdeterminesthedesignmethodologyofit.Inthispaper,thepropagationcharacterofelectromagnetic(EM)waveinthenearsurfaceWUSNisanalyzed,aswellasthepathlossmodelofitisgiven.Inaddition,theinfluenceofhuman'sankletothechannelcharacteristicsofnearsurfaceWUSNisinvestigatedbyelectromagnetictheoryanalysis,simulationandexperiment.AnovelpathlossmodelofnearsurfaceWUSNwhichtakestheinterferenceofhuman'sankleintoconsiderationisproposed.ItisverifiedthattheexistingofhumanabovetheWUSNsystemmaycauseadditionalattenuationtothesignalofnearsurfaceWUSNwhichpropagatesaslateralwavealongtheground.Moreover,therelationoftheattenuationandoperatingfrequencyisdeduced,whichgivesareferencetoextendthefrequencybandappliedinWUSN.

简介：在HSV颜色空间的人的皮肤的特征广泛地基于内容在图象检索的区域被使用。因为它的价值为肤色有一个狭窄的范围，H作为基本识别参数被选择并且当照明紧张或皮肤表面的弯曲正在变化时，能保持稳定。有在HSV颜色空格的照明的变化的参数的规则被学习。当照明紧张被改变时，/2能使稳定的浸透和价值或皮肤表面(S+V)的平均数被变化，这第一被发现，并且(S+V)/2随着肤色变化，但是变化的趋势与H的相反。因此，(S+V)/H能被用作能提高HSV能力认出人的皮肤的一个新识别参数。CLC数字TN911.73

简介：都市化的过程被人的常规运动形成。它在一个城市里产出不同功能的地区，例如居住地区和商业地区。因而，在那里存在在人的活动性模式和城市之间的一个靠近的连接是地区。然而，收集能精确捕获在个人的运动和地区性的功能之间的内在的关系的大规模社会宽的数据不是容易的。因此，我们为理解人的活动性的基本模式的知识仍然是有限的。以便在一个城市里发现不同区域的功能，我们建议一种亲密关系在这份报纸基于方法。亲密关系是为在一个复杂网络测量二个连接节点的关联的一个最近介绍的度量标准。建议模型由经由相对的熵测量用户的到达/离开分发组织不同功能的地区。除了这，我们也由拿核密度评价(KDE)识别每个功能的地区的紧张方法。最后，一些实验被进行与大规模真实数据集评估我们的方法，它从一个月的一个时期由300万个蜂窝电话用户的记录组成。我们在活动性模式和地区性的函数之间的相互作用上的调查结果能高效地捕获城市动力学并且为城市的规划者提供珍贵引用。

简介：ToovercometheshortcomingsoftheLeeimageenhancementalgorithmanditsimprovementbasedonthelogarithmicimageprocessing(LIP)model,thispaperproposeswhatwebelievetobeaneffectiveimageenhancementalgorithm.Thisalgorithmintroducesfuzzyentropy,makesfulluseofneighborhoodinformation,fuzzyinformationandhumanvisualcharacteristics.Toenhanceanimage,thispaperfirstcarriesoutthereasonablefuzzy-3partitionofitshistogramintothedarkregion,intermediateregionandbrightregion.Itthenextractsthestatisticalcharacteristicsofthethreeregionsandadaptivelyselectstheparameterαaccordingtothestatisticalcharacteristicsoftheimage’sgray-scalevalues.Italsoaddsausefulnonlineartransform,thusincreasingtheubiquityofthealgorithm.Finally,thecausesforthegray-scalevalueovercorrectionthatoccursinthetraditionalimageenhancementalgorithmsareanalyzedandtheirsolutionsareproposed.Thesimulationresultsshowthatourimageenhancementalgorithmcaneffectivelysuppressthenoiseofanimage,enhanceitscontrastandvisualeffect,sharpenitsedgeandadjustitsdynamicrange.

简介：分泌物参数通过小移动手持金属杆从控告的人的身体在静电的分泌物(ESD)被测量并且计算。关联学习与费用电压以及途径速度在分泌物参数上被执行了。在费用电压800V，分泌物参数的差别由电极的快、慢的途径速度引起了被发现到达极端价值。为这种特殊情况探索原因，与一个短差距的ESD模型一起的分析被执行。

简介：在不同活动用户dailylife的电话放置的差异增加由使用手机加速表数据认出人的活动的困难。解决这个问题，认出基于的人的活动的一个压缩察觉到的方法压缩了察觉到理论并且利用两个未加工的手机加速表数据和电话放置信息被建议。首先，一个在完全上的字典矩阵用用电话放置信息标记的足够的未加工的tri轴加速数据被构造。然后，稀少的系数为需要被解决L1最小化测试的样品被评估。最后，剩余价值被计算，最小的价值作为指示物被选择获得识别结果。试验性的结果证明这个方法能完成到达89.86%的识别精确性，它比不为识别过程采用电话放置信息的一个识别方法的高。建议方法的识别精确性有效、令人满意。