简介：Biomimetics(orbionics)istheengineeringdisciplinethatconstructsartificialsystemsusingbiologicalprinciples.Theidealfinalresultinbiomimeticsistocreatealivingmachine.Butwhatarethedesirableandnon-desirablepropertiesofbiomimeticproduct?Wherecannaturalprototypesbefound?Howcantechnicalsolutionsbetransferredfromnaturetotechnology?CanweuselivingnaturelikeLEGObricksforconstructionourmachines?Howcanbiologyhelpus?Whatisalivingmachine?Inbiomimeticpracticeonlysome"part"(organ,partoforgan,tissue)oftheobservedwholeorganismisutilized.Apossibletemplateforfuturesuper-organismextensionforbiomimeticmethodsmightbedrawnfromexperimentsinholisticecologicalagriculture(ecologicaldesign,permaculture,ecologicalengineering,etc.).ThenecessarytranslationoftheserulestopracticalactioncanbeachievedwiththeRussianTheoryofInventiveProblemSolving(TRIZ),specificallyadjustedtobiology.Thus,permaculture,reinforcedbyaTRIZconceptualframework,mightprovidethebasisforSuper-OrganismicBionics,whichishypothesizedasnecessaryforeffectiveecologicalengineering.Thishypothesisissupportedbyacasestudy-thedesignofasustainableartificialnaturereserveforwildpollinatorsasalivingmachine.

简介：Whencharacterizingflowsinminiaturizedchannels,thedeterminationofthedynamiccontactangleisimportant.Bymeasuringthedynamiccontactangle,theflowpropertiesoftheflowingliquidandtheeffectofmaterialpropertiesontheflowcanbecharacterized.Amachinevisionbasedsystemtomeasurethecontactangleoffrontorrearmenisciofamovingliquidplugisdescribedinthisarticle.Inthisresearch,transparentflowchannelsfabricatedonthermoplasticpolymerandsealedwithanadhesivetapeareused.Thetransparencyofthechannelsenablesimagebasedmonitoringandmeasurementofflowvariables,includingthedynamiccontactangle.Itisshownthatthedynamicanglecanbemeasuredfromaliquidflowinachannelusingtheimagebasedmeasurementsystem.AnimageprocessingalgorithmhasbeendevelopedinaMATLAB?environment.Im-agesaretakenusingaCCDcameraandthechannelsareilluminatedusingacustommaderinglight.Twofittingmethods,acircleandtwoparabolas,areexperimentedandtheresultsarecomparedinthemeasurementofthedynamiccontactangles.

简介：Nonverbalandnoncontactbehaviorsplayasignificantroleinallowingservicerobotstostructuretheirinteractionswithhumans.Inthispaper,anovelhuman-mimicmechanismofrobot’snavigationalskillswasproposedfordevelopingsociallyacceptableroboticetiquette.Basedonthesociologicalandphysiologicalconcernsofinterpersonalinteractionsinmovement,severalcriteriainnavigationwererepresentedbyconstraintsandincorporatedintoaunifiedprobabilisticcostgridforsafemotionplanningandcontrol,followedbyanemphasisonthepredictionofthehuman’smovementforadjustingtherobot’spre-collisionnavigationalstrategy.Thehumanmotionpredictionutilizesaclustering-basedalgorithmformodelinghumans’indoormotionpatternsaswellasthecombinationofthelong-termandshort-termtendencypredictionthattakesintoaccounttheuncertaintiesofbothvelocityandheadingdirection.Bothsimulationandreal-worldexperimentsverifiedtheeffectivenessandreliabilityofthemethodtoensurehuman’ssafetyandcomfortinnavigation.Astatisticalusertrialsstudywasalsogiventovalidatetheusers’favorableviewsofthehuman-friendlynavigationalbehavior.

简介：TheproblemofflappingmotioncontrolofMicroAirVehicles(MAVs)withflappingwingswasstudiedinthispaper.Basedupontheknowledgeofskeletalandmuscularcomponentsofhummingbird,adynamicmodelforflappingwingwasdeveloped.AcontrolschemeinspiredbyhumanmemoryandlearningconceptwasconstructedforwingmotioncontrolofMAVs.Thesalientfeatureoftheproposedcontrolliesinitscapabilitiestoimprovethecontrolperformancebylearningfromexperienceandobservationonitscurrentandpastbehaviors,withouttheneedforsystemdynamicinformation.Furthermore,theoverallcontrolschemehasafairlysimplestructureanddemandslittleonlinecomputations,makingitattractiveforreal-timeimplementationonMAVs.Boththeoreticalanalysisandcomputersimulationconfirmsitseffectiveness.

简介：Inthepost-genomicbiologyera,thereconstructionofgeneregulatorynetworksfrommicroarraygeneexpressiondataisveryimportanttounderstandtheunderlyingbiologicalsystem,andithasbeenachallengingtaskinbioinformatics.TheBayesiannetworkmodelhasbeenusedinreconstructingthegeneregulatorynetworkforitsadvantages,buthowtodeterminethenetworkstructureandparametersisstillimportanttobeexplored.Thispaperproposesatwo-stagestructurelearningalgorithmwhichintegratesimmuneevolutionalgorithmtobuildaBayesiannetwork.Thenewalgorithmisevaluatedwiththeuseofbothsimulatedandyeastcellcycledata.Theexperimentalresultsindicatethattheproposedalgorithmcanfindmanyoftheknownrealregulatoryrelationshipsfromliteratureandpredicttheothersunknownwithhighvalidityandaccuracy.