Advances in Computational Algorithms and Data Analysis PDF
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Advances in Computational Algorithms and Data Analysis LectureNotesinElectricalEngineering Volume14 Forothertitlespublishedinthisseries,goto http://www.springer.com/7818 Sio-Iong Ao Burghard Rieger Su-Shing Chen • • Editors Advances in Computational Algorithms and Data Analysis (cid:65)(cid:66)(cid:67) Editors Sio-IongAo Su-ShingChen InternationalAssociationofEngineers DepartmentofComputer&Information Unit1,1/F,37-39HungToRoad Science&Engineering(CISE) HongKong UniversityofFlorida HongKong/PRChina POBox116120 GainesvilleFL32611-6120 E450,CSEBuilding BurghardRieger USA UniversitätTrier FBIILinguistische Datenverarbeitung Computerlinguistik Universitätsring15 54286Trier Germany ISBN:978-1-4020-8918-3 e-ISBN:978-1-4020-8919-0 LibraryofCongressControlNumber:2008932627 AllRightsReserved (cid:176)c 2009 SpringerScience+BusinessMediaB.V. Nopartofthisworkmaybereproduced,storedinaretrievalsystem,ortransmittedinanyformorby anymeans,electronic,mechanical,photocopying,microfilming,recordingorotherwise,withoutwritten permissionfromthePublisher,withtheexceptionofanymaterialsuppliedspecificallyforthepurpose ofbeingenteredandexecutedonacomputersystem,forexclusiveusebythepurchaserofthework. Printedonacid-freepaper 9 8 7 6 5 4 3 2 1 springer.com Contents 1 Scaling Exponent for the Healthy and Diseased Heartbeat: QuantificationoftheHeartbeatIntervalFluctuations .............. 1 ToruYazawaandKatsunoriTanaka 2 CLUSTAG&WCLUSTAG:HierarchicalClusteringAlgorithms forEfficientTag-SNPSelection ................................ 15 Sio-IongAo 3 TheEffectsofGeneRecruitmentontheEvolvability andRobustnessofPattern-FormingGeneNetworks .............. 29 AlexanderV.SpirovandDavidM.Holloway 4 ComprehensiveGeneticDatabaseofExpressedSequenceTags forCoccolithophorids........................................ 51 MohammadRanjiandAhmadR.Hadaegh 5 HybridIntelligentRegressionswithNeuralNetworkandFuzzy Clustering ................................................. 65 Sio-IongAo 6 DesignofDroDeASys(DrowsyDetectionandAlarmingSystem) .... 75 HrishikeshB.Juvale,AnantS.Mahajan,AshwinA.Bhagwat, VishalT.Badiger,GaneshD.Bhutkar,PriyadarshanS.Dhabe, andManikraoL.Dhore 7 The Calculation of Axisymmetric Duct Geometries for Incompressible Rotational Flow with Blockage Effects andBodyForces ............................................ 81 VasosPavlika 8 FaultTolerantCacheSchemes................................. 99 H.-yu.TuandSarahTasneem v vi Contents 9 ReversibleBinaryCodedDecimalAddersusingToffoliGates ...... 117 RekhaK.James,K.PouloseJacob,andSreelaSasi 10 Sparse Matrix Computational Techniques in Concept DecompositionMatrixApproximation.......................... 133 ChiShenandDuranWilliams 11 TransferableE-cheques:AnApplicationofForward-SecureSerial Multi-signatures ............................................ 147 NagarajaiahR.Sunitha,BharatB.R.Amberker, andPrashantKoulgi 12 AHiddenMarkovModelbasedSpeechRecognitionApproach toAutomatedCryptanalysisofTwoTimePads................... 159 LiaqatAliKhanandM.S.Baig 13 AReconfigurableandModularOpenArchitectureController: TheNewFrontiers .......................................... 169 MuhammadFarooq,DaoBoWang,andN.U.Dar 14 AnAdaptiveMachineVisionSystemforParts AssemblyInspection......................................... 185 JunSun,QiaoSun,andBrianSurgenor 15 Tactile Sensing-based Control System for Dexterous Robot Manipulation............................................... 199 HanafiahYussof,MasahiroOhka,HirofumiSuzuki, andNobuyukiMorisawa 16 ANovelKinematicModelforRoughTerrainRobots.............. 215 JosephAuchter,CarlA.Moore,andAshitavaGhosal 17 BehaviorEmergenceinAutonomousRobotControl byMeansofEvolutionaryNeuralNetworks ..................... 235 RomanNeruda,StanislavSlusˇny´,andPetraVidnerova´ 18 SwarmEconomics........................................... 249 SanzaKazadiandJohnLee 19 MachinesImitatingHumans:AppearanceandBehaviour inRobots................................................... 279 QaziS.M.Zia-ul-Haque,ZhiliangWang,andXueyuanZhang 20 ReinforcedART(ReART)forOnlineNeuralControl ............. 293 DamjeeD.EdiriweeraandIanW.Marshall 21 TheBumpHuntingbytheDecisionTree withtheGeneticAlgorithm ................................... 305 HideoHirose Contents vii 22 MachineLearningApproachesfortheInversionoftheRadiative TransferEquation........................................... 319 EstebanGarcia-Cuesta,FernandodelaTorre,andAntonioJ.deCastro 23 EnhancingthePerformanceofEntropyAlgorithm usingMinimumTreeinDecisionTreeClassifier.................. 333 KhalafKhatatnehandIbrahiemM.M.ElEmary 24 NumericalAnalysisofLargeDiameterButterflyValve ............ 349 ParkYoungchulandSongXueguan 25 AxialCrushingofThin-WalledColumnswithOctagonalSection: ModelingandDesign ........................................ 365 YuchengLiuandMichaelL.Day 26 AFastStateEstimationMethodforDCMotors .................. 381 GabrielaMamani,JonathanBecedas,VicenteFeliu, andHeberttSira-Ram´ırez 27 FlatnessbasedGPIControlforFlexibleRobots .................. 395 JonathanBecedas,VicenteFeliu,andHeberttSira-Ram´ırez 28 EstimationofMass-Spring-DumperSystems .................... 411 JonathanBecedas,GabrielaMamani,VicenteFeliu, andHeberttSira-Ram´ırez 29 MIMOPIDControllerSynthesiswithClosed-LoopPole Assignment ................................................ 423 Tsu-ShuanChangandA.NazliGu¨ndes¸ 30 RobustDesignofMotorPWMControlusingModeling andSimulation ............................................. 439 WeiZhan 31 Modeling,ControlandSimulationofaNovelMobile RoboticSystem ............................................. 451 XiaoliBai,JeremyDavis,JamesDoebbler,JamesD.Turner, andJohnL.Junkins 32 AllCircuitsEnumerationinMacro-EconometricModels .......... 465 Andre´ A.Keller 33 NoiseandVibrationModelingforAnti-LockBrakeSystems ....... 481 WeiZhan 34 InvestigationofSinglePhaseApproximationandMixtureModel onFlowBehaviourandHeatTransferofaFerrofluidusingCFD Simulation ................................................. 495 MohammadMousavi viii Contents 35 TwoLevelParallelGrammaticalEvolution...................... 509 PavelOsˇmera 36 Genetic Algorithms for Scenario Generation in Stochastic Programming:MotivationandGeneralFramework ............... 527 JanRoupecandPavelPopela 37 NewApproachofRecurrentNeuralNetworkWeightInitialization.. 537 RobertoMarichal,J.D.Pin˜eiro,E.J.Gonza´lez,andJ.M.Torres 38 GAHC:HybridGeneticAlgorithm............................. 549 RadomilMatousek 39 ForecastingInflationwiththeInfluenceofGlobalization usingArtificialNeuralNetwork-basedThinandThickModels ..... 563 Tsui-FangHu,IkerGondraLuja,Hung-ChiSu,andChin-ChihChang 40 Pan-TiltMotionEstimationUsingSuperposition-TypeSpherical Compound-LikeEye......................................... 577 Gwo-LongLinandChi-ChengCheng Chapter 1 Scaling Exponent for the Healthy and Diseased Heartbeat Quantification of the Heartbeat Interval Fluctuations ToruYazawaandKatsunoriTanaka* Abstract“Alternans”isanarrhythmiaexhibitingalternatingamplitudeoralternat- ingintervalfromheartbeattoheartbeat,whichwasfirstdescribedin1872byTraube. Recently alternans was finally recognized as the harbinger of a cardiac disease because physicians noticed that an ischemic heart exhibits alternans. To quantify irregularityof the heartbeat including alternans, weused thedetrended fluctuation analysis(DFA).Werevealedthatinboth,animalmodelsandhumans,thealternans rhythmlowersthescalingexponent.Thiscorrespondencedescribesthatthescaling exponentcalculatedbytheDFAreflectsariskforthe“failing”heart. Keywords Alternans·Animalmodels·Crustaceans·DFA·Heartbeat 1.1 Introduction My persimmon tree bears rich fruits every other year. A climatologist report that globalatmosphericoxygenhasbistability[1].Period-2issuchanintriguingrhythm innature.Thecardiac“Alternans”isanotherperiod-2.Incardiacperiod-2,theheart- beat is alternating the amplitude/interval from beat to beat on the electrocardio- gram (EKG). Alternans has remained an electrocardiographic curiosity for more thanthreequartersofacentury[2,3].Recently,alternansisrecognizedasamarker for patients at an increased risk of sudden cardiac death [2–7]. In our physiologi- cal experiments on the hearts in the 1980s, we have noticed that alternans is fre- quentlyobservablewiththe“isolated”heartsofcrustaceans(Note:Atthisisolated T.Yazawa((cid:1))andK.Tanaka DepartmentofBiologicalScience,TokyoMetropolitanUniversity,Tokyo,Japan. Bio-PhysicalCardiologyReseachGroup e-mail:[email protected];[email protected] ∗Thecontactauthormailingaddress:1705-6-301Sugikubo,Ebina,243-0414Japan,telephoneand faxnumber:+81-462392350.Presentaddress,228-2Dai,Kumagaya,Saitama,360–0804Japan. S.-I.Aoetal.(eds.),AdvancesinComputationalAlgorithmsandDataAnalysis, 1 LectureNotesinElectricalEngineering14, (cid:1)c SpringerScience+BusinessMediaB.V.2009 2 T.YazawaandK.Tanaka conditiontheheartsoonerorlaterdiesintheexperimentaldish).Wesoonrealized thatalternansisasignoffuturecardiaccessation.Presently,someauthorsbelieve thatitistheharbingerforsuddendeath[2,6].Sowecamebacktothecrustaceans, because we knew the crustaceans are outstanding models. However, details of al- ternanshavenotbeenstudiedincrustaceans.Soweconsideredthatwecouldstudy this intriguing rhythm by the detrended fluctuation analysis (DFA), since we have already demonstrated that the DFA can distinguish a normal heart (intact heart) from an unhealthy heart (isolated heart) in animal models [8]. We finally revealed that alternans and lowered scaling exponent occurred concurrently. In this report, wedemonstratethattheDFAisanadvantageoustoolinanalyzing,diagnosingand managingthedysfunctionoftheheart. 1.2 Procedure 1.2.1 DFAMethods:Background The DFA is an analytical method in physics, based on the concept of “scaling” [9,10]. The DFA was applied to understand a “critical phenomenon” [9,11,12]. Systems near critical points exhibit self-similar properties. Systems that exhibit self-similarpropertiesarebelievedtobeinvariantunderatransformationofscale. Finally the DFA was expected to apply to any biological system, which has the propertyofscaling. Stanley and colleagues have considered that the heartbeat fluctuation is a phe- nomenon,whichhasthepropertyofscaling.Theyfirstappliedthescaling-concept to a biological data in the late 1980s to early 1990s [11,12]. They emphasized on itspotentialutilityinlifescience[11].However,althoughthenonlinearmethodis increasingly advancing, a biomedical computation on the heart seems not to have maturedtechnologically.Indeedwestillaskus:Canwedecodethefluctuationsin cardiacrhythmstobetterdiagnoseahumandisease? 1.2.2 DFAMethods We made our own programs for measuring the beat-to-beat intervals and for cal- culating the approximate scaling exponent of the interval time series. These DFA- computation methods have already been explained elsewhere [13]. We describe it herebrieflyonthemostbasiclevel. Firstly, we obtain the heartbeat data digitized at 1KHz. About 3,000beats are necessary for a reliable calculation of an approximate scaling exponent. Usually a continuousrecordforabout50minatasingletestingisrequired.WeuseanEKG orfingerpressurepulses.
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