Smart Manufacturing: Internet of Things, Real-Time ...sosengineering.org/2018/wp-content/uploads/2014/07/... · 1G (2.4) (1980s) Promulgated By Japan’s NTT; Basic Analog Voice Service;

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SmartManufacturing:InternetofThings,Real-TimeDecisionMaking,andArtificialIntelligence*ØIoT:Servgoods,Wireless,PowerØRTDM:Sensing,Processing,Reacting,LearningØAI:History,Learning,DecisionMakingØConcludingRemarks

JamesM.Tien,PhD,DEng(h.c.),NAEDistinguishedProfessor&DeanEmeritusCollegeofEngineering,UniversityofMiami

*J.M.Tien(2017),“InternetofThings,Real-TimeDecisionMaking,andArtificialIntelligence”,AnnalsofDataScience,4(2),pp.149-178,June2017

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IoT:InternetofConnectedServgoods (in3D)

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SENSOR

SERVICE

GOOD


IoT:InternetofConnectedServgoods (in4D)

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IoT:Goods,Services,Servgoods,ConnectedServgoods

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FOCUS GOODS SERVICES SERVGOODS CONNECTEDSERVGOODS

Production Pre-Produced Co-Produced Demand-Produced Internet-Compatible

Variability Identical Heterogeneous Assorted Assorted

Physicality Tangible Intangible Mixed Mixed

Product “Inventoryable” Perishable Identifiable Identifiable

Objective Reliable Personalizable Adaptable Connectable

Satisfaction Utility-Related Expectation-Related Satisfaction-Related Status-Related

LifeCycle Recyclable Reusable Flexible Agile

Manufacture Products Processes Smart Products Interconnected

Example Car Electronic-Assists Electronic-AssistedCar SmartDriverlessCar


IoT:StagesinANation’sTechnicalEvolution

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CHARACTERISTICS MECHANICAL ELECTRICAL INFORMATION INTERNET

EconomicFocus Agriculture;Mining

Manufacturing;Construction

Services ConnectedServgoods

ProductivityFocus Farming Factory Knowledge Collaboration

TechnologyFocus Tools Machines Computers Communications

HumanPower Muscle Muscle;Brain Brain Brain;Behavior

LivingStandard Subsistence QualityofGoods QualityofLife QualityofInteraction

ImpactScope Family Nation Global Universal

USOnset Late1700s Late1800s Late1900s Early2000s


IoT:Top10Smartest“NewOpportunity”Companiesin2016

(Source: MIT Tech Review)

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FOCUS SCOPE GOODS SERVICES SERVGOODS CONNECTEDSERVGOODS

1.Amazon E-commerce(dronedeliveries)

X X X X

2.Baidu SearchEngine X X X X

3.Illumina DNAsequencing --- X --- ---

4.Tesla Electriccars X X X X

5.Aquion Energy Powergridbatteries X --- --- ---

6.Mobileye Driverassisttech X X X X

7.23andMe ConsumerDNAtests --- X --- ---

8.Alphabet Google Autonomous Vehicles X X X X

9.SparkTherapeutics GeneTherapy --- X --- ---

10.Huawei Smartphone X X X X


IoT:ExampleConnectionsforSmartManufacturing

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Connection ExampleServgoods

HumantoHuman(H2H)

CellularCommunication;ElectronicVoiceCommunication;ShortMessageService;Intercom;Web-BasedSpeakerSystem;BlueToothCommunication

HumantoMachine(H2M)

E-Commerce;WorldWideWeb;SocialMedia;Point-of-Sale;Laptop;Webcam;SecurityCamera;AutomaticTellerMachine(ATM);OccupancySensor;

MachinetoHuman(M2H)

Radio;Television;E-Billboard;E-Magazine;HighPowerWi-Fi;RadioFrequencyIdentification(RFID);VideoInternetProtocolPhone;AlarmSystem;DigitalSignage

MachinetoMachine(M2M)

NetworkedSensor;ConnectedDevice;VideoConferencing;EmbeddedArtificiallyIntelligentDevice;EnvironmentalMonitoring;Drone;AutonomousVehicle


IoT:FrequencySpectrumforSensorsandApplications

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RangeinGHz ExampleApplications

0.0– 1.0 BroadcastTV(3KHz),AMRadio(540-1600KHz),FMRadio(88.1-108.1MHz),CellPhone(800MHz)

1.0– 2.0 Cell Phone(1.0GHz),GPS(1.22GHz,1.57GHz)

2.0– 300.0 WiFi (2.4 GHz,3 GHz,5 GHz,60 GHz),SatelliteRadio(2.3GHz),MicrowaveOven,Bluetooth

Above300.0 Infrared,VisibleLight,Ultraviolet,X-Rays,GammaRays


IoT:GlobalWirelessConnections

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Year A.Connections(InBillions)

B.Subscribers(InBillions) A/BRatio

2000 0.7 0.6 1.2

2005 2.2 1.6 1.4

2010 5.4 2.8 1.9

2015 8.6 4.0 2.2


IoT:GenerationsofWirelessNetworks

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Speedinkbps DescriptionofFeatures(DecadeofPromulgation)

1G(2.4) (1980s)PromulgatedByJapan’sNTT;BasicAnalogVoiceService;GrowthFueledByCellularPhones(AnAreaisDividedIntoCells);AnalogProtocols(MTS,AMTS,PTT,IMTS);Issues(PossibleEavesdroppingbyAll-BandRadioReceiver)

2G(64) (1990s)PromulgatedByFinland’sRadiolinja;BasicDigitalVoiceService;ImprovedCoverageandCapacity;FirstDigitalStandards(GPRS,GSM,CDMA,EDGE);Benefits(DigitalEncryption,ShortMessageService)

3G(2K) (2000s)PromulgatedByIMT– InternationalMobileTelecommunications;BasicVoiceServiceWithData(Text,Multimedia);FirstMobileBroadband(WideBandCDMA,WLAN,BlueTooth,GlobalRoaming,UMTS,HSDPA,Internet,VideoConferencingCalls

4G(100K) (2010s)PromulgatedByIMT-Advanced;HighMobilityCommunications(Cars,Trains,HDMobileTV);GrowthFueledBySmartphones;PrimarilyforDigitalData;Internet-BasedProtocols(WiMax,LTE);TrueMobileBroadband

5G(YetToBeDetailed)

(2020s)ExpectedBySouthKorea;FasterSpeed(e.g.,downloadanHDmoviein1secondversustoday’s10minutes);MoreReliableService;Millimeter Waves;SmallCells;MassiveMIMO;Beamforming;FullDuplex;RobustGPS


IoT:PowerLevelandFrequencySpectrumApplications

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PowerLevel IEEEStandards&ExampleApplications

UpTo15.4Watts IEEE802.3AF;2PairCables;802.11nWAPs,ThinClients,IPPhones,

UpTo30Watts IEEE802.3AT;2PairCables;IPCameras,Alarms,VideoIPPhones,RFIDReaders

UpTo60Watts IEEE802.3BT;4PairCables;802.11acWAPs,AccessControl,IPCameras,PoS Readers

UpTo100Watts IEEE802.3BT;4PairCables;Televisions,DesktopComputers,Projectors


RTDM:Real-TimeDecisionInformaticsforGoodsandServices

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RTDM:FirstDecadeofBigData

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Source: International DataCorporation


RTDM:ComponentsofTheThirdIndustrialRevolution

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BIGDATAANALYTICS:MATHEMATICS,PHYSICS,COMPUTING

ADAPTIVESERVICES SMARTMANUFACTURING

MASSCUSTOMIZATION:INTEGRATEDGOODS,SERVICESORSERVGOODS


RTDM:AdaptiveServicesEnablers

DIMENSION DEFINITION CHARACTERISTICS ELEMENTS

Monitoring DegreeofSensedActions

Ø DataCollectionØ DataAnalysisØ Abstraction

Ø Sensors;Agents;SwarmsØ Structuring;Processing;MiningØ Derivations;Groupings;Patterns

Feedback DegreeofExpectedActions

Ø StandardizedØ ProceduralØ Algorithmic

Ø Pre-Structured;Pre-PlannedØ Policies;StandardOperatingProceduresØ Optimized;Bayesian

Cybernetics DegreeofReactiveActions

Ø DeterministicØ DynamicØ Adaptive

Ø KnownStates;DeterministicActionsØ KnownStateDistributions;DynamicActionsØ UnknownStates;AdaptiveActions

Learning DegreeofUnstructuredActions

Ø CognitionØ Evidence-BasedØ Improvisation

Ø Recognition-Based;BehavioralØ Information-Based;GeneticØ Experience-Based;Evolutionary

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RTDM:SmartManufacturingEnablers

ENABLER METHODSOBJECTIVES

EFFICIENCY EFFECTIVENESS

CloudComputing

Software:Unlimited;Algorithmic;Simulation P P

Hardware:UbiquitousComputing;Scalable P P

Cost:OnDemand,Pay-As-You-Use;CyberSecurity P P

NovelMaterials

Creation:BigDataAnalytics;DecisionInformatics P P

Technologies:Nanotechnology;SmartSensing P P

Cost:Toxicity;EnvironmentalImpact P P

SmartRobotics

Software:DigitizedDesign;SmartControls P P

Hardware:Autonomous/Smart Sensors;3DPrinting P P

Evolution:Cheaper;MoreEfficient;MoreDistributed P P

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RTDM:TraditionalVersusBigDataApproaches

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COMPONENTS ELEMENTS TRADITIONALAPPROACH BIGDATAAPPROACH

AcquisitionFocusEmphasisScope

Problem-OrientedDataQualityRepresentativeSample

Data-OrientedDataQuantityCompleteSample

AccessFocusEmphasisScope

On-Supply,Local-ComputingOver-TimeAccessibilityPersonal-Security

On-Demand,Cloud-ComputingReal-TimeAccessibilityCyber-Security

AnalyticsFocusEmphasisScope

AnalyticalEleganceCausativeRelationshipData-Poor,Information-Poor(DPIP)

AnalyticalMessinessCorrelativeRelationshipData-Rich,Information-Unleashed(DRIU)

ApplicationFocusEmphasisScope

Steady-StateOptimalityModel-DrivenObjectiveFindings

Real-TimeFeasibilityEvidence-DrivenSubjectiveFindings


RTDM:BigDataLimitations

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COMPONENTS ELEMENTS POTENTIALCONCERNS

AcquisitionFocusEmphasisScope

BigDataDoesNotImplyBig/CompleteUnderstandingofUnderlyingProblemBigDataQuantityDoesNotImplyBigDataQualityBigDataSampleDoesNotImplyARepresentativeorEvenACompleteSample

AccessFocusEmphasisScope

BigData’sOn-DemandAccessibilityMayCreatePrivacyConcernsBigData’sReal-TimeAbilitiesMayObscurePastandFutureConcernsBigData’sCyber-SecurityConcernsMayOverlookPersonal-SecurityConcerns

AnalyticsFocusEmphasisScope

BigData’sInherentMessinessMayObscureUnderlyingRelationshipsBigData’sCorrelationalFindingMayResultInAnUnintendedCausalConsequenceBigData’sUnleashingofInformationMayObscureUnderlyingKnowledge

ApplicationFocusEmphasisScope

BigData’sFeasibleExplanationsMayObscureMoreProbableExplanationsBigData’sEvidence-DrivenFindingsMayObscureUnderlyingFactualKnowledgeBigData’sSubjective,Consumer-CentricFindingsMayObscureOtherFindings


RTDM:BigDataExampleAnalytics&Efforts

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SCOPE EXAMPLE ANALYTICS EXAMPLE EFFORTS

Correlational Analysis

Statistics; VisualizationOperations Research; SimulationManagement Science; Algorithms

Data Fusion; Visualization Cave; SAS; IBM; GE VMware; Terradata; Amazon; Coca-ColaSplunk; Twitter; Zynga

Pattern Recognition

Tracking; Disease SpreadTopology; Simulation; ModelingReal-Time Search

ShopperTrak; Facebook’s Timeline; GoogleAyasdi’s Software; Ansys’ Simulator; SolidWorksFast Fourier Transform; IBM’s Watson (Jeopardy)

Evidence-Driven

Marketing (Behavior, Attitude);Predicting (Savvy, Statistics);Software Agent; Answering Questions

Facebook’s Graph Search; Microsoft; IBM; Oracle; Dell; CrowdsourcingApple’s Siri; Google’s MapReduce; Hadoop


RTDM:FromDatatoWisdom

DATA INFORMATION KNOWLEDGE INSIGHTS/WISDOM

Operational Tactical Strategic Systemic

Decision Making Range

• Data:Basicobservation;measurements,transactions,etc.

• Information:Processeddata;derivations,groupings,patterns,etc.

• Knowledge:Processedinformationplusexperiences,beliefs,values,culture;explicit,tacit/conscious,unconscious.

• Insights/Wisdom:Processedknowledgeplusassessmentsovertimeandspace.

ØAtPresent,FromADataPoor,InformationPoor(DPIP) toADataRich,InformationPoor(DRIP),toADataRich,InformationUnleashed(DRIU)Environment

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AI:ExampleSmartServgoodsAICategory Definition;ExampleServgoods

VirtualAssistants

SoftwareAgentsThatCanUnderstandNaturalLanguageandActAppropriately;Amazon’sEcho/Alexa,Apple’sHomeKit/Siri,Google’sHome/Assistant,Microsoft’sDigitalAssistant/Cortana

WebBots SoftwareApplicationsThatRunsAutomatedTasks(i.e.,Scripts)OvertheInternet;WebCrawler,SearchEngine,FacebookBot,Chatterbot,TwitterBot,MaliciousBot(Botnet,ZombieBot,TicketingAccumulatingBot,Spambot)

Robots ProgrammableMachinesCapableofCarryingOutaComplexSeriesofActionsAutomatically;AutonomousVehicles,Drones,WeldingRobots,MilitaryQuadrupedalMachines,MiningRobots,CollaborativeRobots

MedicalDevices

AppliancesIntendedforDiagnosticand/orTherapeuticPurposes;Pacemaker,InsulinPump,GlucoseMonitor,Defibrillator,daVinciSurgicalAssistSystem,RehabilitationRobot,TelepresenceRobot,PharmacyAutomation,Cloud-BasedHealthMonitoringSystem

Platforms AllowUserstoEmployPre-BuiltMachineLearningandDecisionMakingAlgorithmstoDevelopIntelligentApplications;ImageRecognition,NaturalLanguageProcessing,VoiceRecognition,SpeechSynthesis,PredictiveAnalytics,VirtualReality

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AI:TimeLine

Period MajorMilestones

Defining(1950-1975)

1950(AlanTuringintroduces“machinethinking”);1955(JohnMcCarthy,MarvinMinsky,&ClaudeShannonMeetatDartmouthtoCoin“Artificial Intelligence”);1961(MarvinMinskyPublishes“StepsTowardAI”);1972(HubertDreyfusPublishes“WhatComputersCan’tDo”)

Winter(1975-2000)

1979(BackgammonProgramByHansBerlinerDefeatsHumanChampion);1997(IBM’sDeepBlueChessComputerDefeatsWorldChampionGarryKasparovinRematch)

Renaissance(2000-Present)

2004(DARPASponsorsDriverlessCompetitionAcrossMohaveDesert);2011(IBM’sWatsonDefeatsJeopardy!Champions);2016(Google’sAlphaGo BeatsKorea’sGoChampion)

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AI:MachineLearningPeriod MajorMilestones

Background Evolvedfromthestudyofpatternrecognitionandcomputationallearningtheory,machinelearningconcernsthestudyandconstructionofalgorithmsthatcanlearnfromandmakepredictionsondata– suchalgorithmsovercomestrictlystaticprograminstructionsbymakingdata-drivenpredictionsordecisionsorbybuildingamodelfromsampleinputs

Purpose Machinelearningisemployedwhenprogrammingexplicitalgorithms isnotfeasible,includingspamfiltering,detectionofnetworkintruders,maliciousdatamanipulators,opticalcharacterreaders,voice recognitionassistants, etc.;itcanbeunsupervised,asindataminingandcomputationalstatistics(e.g.,Microsoftemploys5ANN– seebelow–layerstoanalyzespeech)

Approaches decisiontree;associationrule;artificial neuralnetwork(ANN); deep(multiplehiddenlayers)ANN;supportvectormachine;Bayesiannetwork;geneticalgorithmthatmimicstheprocessofnaturalselection

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AI:DecisionMakingPeriod MajorMilestones

Framework Inanenvironmentthatchangesovertimeeitherduetopreviousactionsorduetoeventsthatareoutsidethecontrolofthedecisionmaker,decisionsaretypicallymorecomplex andmustoccurinreal-time: averyappropriateframework foremployingstrongAI(i.e.,arecursiveapproachthatcanemulatehumanintelligence),especiallyifpairedwithhumans(whopossessthetraitsofhonesty,fidelity,morality,dependability,trustworthiness,steadfastness,persistence,etc.)

Myths/Concerns

Atkinson(2016)attemptstodebunkfiveprevailingmythsaboutAIanditsdetrimentalimpactontheworld’seconomyandsociety-at-large:i)AIwill destroymostjobs,ii)AIwillmakehumansstupid,iii)AIwill destroypeople’sprivacy,iv)AIwill enablebiasandabuse,andv)AIwill eventuallyexterminatehumanity

RecentProgress

AI(witha80%accuracy)hassurpassedhumanphysicians(witha60percentaccuracy)inpredictingwhenpatientswithheartconditionswilldiebyanalyzingtheirbloodtestsand3Dheartmodels;Google’sAIhassignificantlyimprovedthediagnosisofeyediseaseandthesameistruethedetectionofbreastcancer; employingAI toidentifyfakenews,diagnosementaldisorders(e.g.,schizophrenia,depression,bipolardisorder,etc.),enhancehuman-machineinterface(e.g.,Microsoft’sSkypetranslator),facilitatemachine-machine interface(e.g.,autonomousvehicles)

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ConcludingRemarksØ IoT,RTDM&AI area)relatedtopicsandcomponentsofSmartManufacturing,b)alldependentonBigData,acorrelational–notcausational– approach,andc)underpinningthetransitionfrommassproductiontomasscustomization

Ø IoT’s 5G(expectedin2030)allowsforreal-time;acombinedmoonshotbyindustry,universities&government

ØRTDM iscriticaltoautonomousvehicles,health,medicine,navigation,“trolley”problem,etc.

ØAI isanothermultidisciplinarymoonshot,initiatedin2000andexpectedtoflourishin2030,coincidentwith5Gintroduction

Ø IoT,RTDM&AI areindividuallyandcollectivelypartofadisruptiveinnovationortransformation,allapartoftheThirdIndustrialRevolutionwitharangeofeconomicbenefits

Ø Privacy,securityandsafetymaybeintentionallyorunintentionallybreachedinaconnectedIoT,RTDM&AI world

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Documents

Smart Manufacturing: Internet of Things, Real-Time ...sosengineering.org/2018/wp-content/uploads/2014/07/... · 1G (2.4) (1980s) Promulgated By Japan’s NTT; Basic Analog Voice Service;