NASA Ames Data Sciences Group - Amazon Web Services · final missed clred msl intercept vectored...
Transcript of NASA Ames Data Sciences Group - Amazon Web Services · final missed clred msl intercept vectored...
![Page 2: NASA Ames Data Sciences Group - Amazon Web Services · final missed clred msl intercept vectored sight gar ... • Given appropriate historical data, have the ability to predict:](https://reader034.fdocuments.us/reader034/viewer/2022050519/5fa28c236d82fc405128800d/html5/thumbnails/2.jpg)
•2
TheDataSciencesGroupatNASAAmes
DataMiningResearchandDevelopment(R&D)forapplicationtoNASAproblems(Aeronautics,EarthScience,SpaceExploration,SpaceScience)
GroupMembersIlya AvrekhKamalika Das,Ph.D.DaveIversonVijayJanakiraman,Ph.D.RodneyMartin,Ph.D.BryanMatthewsDavidNielsenNikunjOza,Ph.D.VeronicaPhillipsJohnStutzHamed Valizadegan,Ph.D.+summerstudents
FundingSources
• ScienceMissionDirectorate:AISTandCMACprograms
• NASAAeronauticsResearchMissionDirectorate- ATD,SMART-NAS,SASOProject
• NASAEngineeringandSafetyCenter
• ExplorationSystemsMissionDirectorate,ExplorationTechnologyDevelopmentProgram
• Non-NASA:DARPA,DoD
Team Members are NASA Employees, Contractors, and Students.
![Page 3: NASA Ames Data Sciences Group - Amazon Web Services · final missed clred msl intercept vectored sight gar ... • Given appropriate historical data, have the ability to predict:](https://reader034.fdocuments.us/reader034/viewer/2022050519/5fa28c236d82fc405128800d/html5/thumbnails/3.jpg)
ExampleDataMiningProblems
• Aeronautics:AnomalyDetection,PrecursorIdentification,textmining(classification,topicidentification)
• EarthScience:Fillinginmissingmeasurements,anomalydetection,teleconnections,climateunderstanding
• SpaceScience:Kepler planetcandidates• SpaceExploration:systemhealthmanagement,vascularstructureidentification
![Page 4: NASA Ames Data Sciences Group - Amazon Web Services · final missed clred msl intercept vectored sight gar ... • Given appropriate historical data, have the ability to predict:](https://reader034.fdocuments.us/reader034/viewer/2022050519/5fa28c236d82fc405128800d/html5/thumbnails/4.jpg)
FourV’sofBig Tough,SleepDeprivingData
ØVolume:Ø RadarTracks:47facilities(1
year)~423GB(Compressed),~3.2TB(CSV)
Ø WeatherandForecast(EntireNAS):CIWS~2.8TB
ØVeracityØ DatadropoutsØ DuplicatetracksØ TrackendinginmidairØ Reusedflightidentifiers
ØVelocityØ RadarTracks:47Facilities
Ø ~35GB/month(compressed).
Ø ~268GB/month(uncompressed)
Ø WeatherandForecast(EntireNAS):CIWS~233GB/month
ØVarietyØ Numerical
(continuous/binary)Ø Weather(forecast/actual)Ø Radar/AirportmetadataØ ATCVoiceØ ASRStextreports
(Pilot/Controller)
AmazingAlgorithm
IntuitiveReports
![Page 5: NASA Ames Data Sciences Group - Amazon Web Services · final missed clred msl intercept vectored sight gar ... • Given appropriate historical data, have the ability to predict:](https://reader034.fdocuments.us/reader034/viewer/2022050519/5fa28c236d82fc405128800d/html5/thumbnails/5.jpg)
AeronauticsDataMiningProblems
• AnomalyDetection– AnomalyDiscoveryoverlargesetofvariables– Particularvariableofinterest,forexample,fuelburn
• Determineexpectedinstantaneousfuelburngivencurrentstateofaircraft
• Comparewithactualinstantaneousfuelburn• Wheredifferenceishigh,problemmaybeoccurring
• PrecursorIdentification– Givenundesirableeffect(e.g.,go-around),identifyprecursors(e.g.,overtakesituation,highspeedapproach)
• Textmining– Textclassification,topicidentification
![Page 6: NASA Ames Data Sciences Group - Amazon Web Services · final missed clred msl intercept vectored sight gar ... • Given appropriate historical data, have the ability to predict:](https://reader034.fdocuments.us/reader034/viewer/2022050519/5fa28c236d82fc405128800d/html5/thumbnails/6.jpg)
TopicExtractionExampleTOPIC1
autopltacftspd
capturemoderatelevel
engagedleveloffvertctl
disconnectedselectedfpmlightclbpitch
manuallywarningpwr
TOPIC2
timedayleg
contributingfactorshrscrewfactorfatiguenighttriprestdutyflyinglonglate
previousincidentlack
alerter
TOPIC3apchrwyvisualilstwrlndglocarptfinal
missedclredmsl
interceptvectoredsightgar
terrainfield
uneventfulctl
Otherexamplesof‘fatigue’
AltitudeDeviationSpatialDeviationRampExcursionLandingwithoutclearanceRunwayIncursionUnstableApproach
![Page 7: NASA Ames Data Sciences Group - Amazon Web Services · final missed clred msl intercept vectored sight gar ... • Given appropriate historical data, have the ability to predict:](https://reader034.fdocuments.us/reader034/viewer/2022050519/5fa28c236d82fc405128800d/html5/thumbnails/7.jpg)
AeronauticsAnomalyDetection:CurrentMethods
Exceedance-BasedMethods• Knownanomalies• Conditionsover2-3variables(e.g.,speed>250knots,altitude=1000ft,landing)
• Cannotidentifyunknownanomalies• Lowfalsepositiverate,highfalsenegative(misseddetection)rate.
![Page 8: NASA Ames Data Sciences Group - Amazon Web Services · final missed clred msl intercept vectored sight gar ... • Given appropriate historical data, have the ability to predict:](https://reader034.fdocuments.us/reader034/viewer/2022050519/5fa28c236d82fc405128800d/html5/thumbnails/8.jpg)
Data-DrivenMethods
• DISCOVERanomaliesby– learningstatisticalpropertiesofthedata– findingwhichdatapointsdonotfit(e.g.,faraway,lowprobability)
– nobackgroundknowledgeonanomaliesneeded
• Complementarytoexistingmethods– Lowfalsenegative(misseddetection)rate– Higherfalsepositiverate(identifiedpoints/flightsunusual,butnotalwaysoperationallysignificant)
• Data-drivenmethods->insights->modificationofexceedance detection
![Page 9: NASA Ames Data Sciences Group - Amazon Web Services · final missed clred msl intercept vectored sight gar ... • Given appropriate historical data, have the ability to predict:](https://reader034.fdocuments.us/reader034/viewer/2022050519/5fa28c236d82fc405128800d/html5/thumbnails/9.jpg)
Example:HighSpeedGo-Around
• OvershootsExtendedRunwayCenterline(ERC)byover1SM
• Over250Kts @2500Ft.• Angleofintercept>40°• Overshoots2nd approach
BryanMatthews,DavidNielsen,JohnSchade,Kennis Chan,andMikeKiniry,AutomatedDiscoveryofFlightTrackAnomalies,33rd DigitalAvionicsSystemsConference,2014
![Page 10: NASA Ames Data Sciences Group - Amazon Web Services · final missed clred msl intercept vectored sight gar ... • Given appropriate historical data, have the ability to predict:](https://reader034.fdocuments.us/reader034/viewer/2022050519/5fa28c236d82fc405128800d/html5/thumbnails/10.jpg)
ProvidingDomainExpertFeedbackActive learning with rationales frameworkTraining
Input Features
MKAD
Nominals
Anomalies
SME
Activelearning strategy
Testing
Input Features
MKAD
Nominals
Anomalies
Rationale features
2-class classification/ranking
algorithm
Uninteresting anomalies
Operationally significant anomalies
Manali Sharma,Kamalika Das,MustafaBilgic,BryanMatthews,DavidNielsen,andNikunjOza,ActiveLearningwithRationalesforIdentifyingOperationallySignificantAnomaliesinAviation,EuropeanConferenceonMachineLearningandPrinciplesandPractices
OfKnowledgeDiscovery(ECML-PKDD),2016
![Page 11: NASA Ames Data Sciences Group - Amazon Web Services · final missed clred msl intercept vectored sight gar ... • Given appropriate historical data, have the ability to predict:](https://reader034.fdocuments.us/reader034/viewer/2022050519/5fa28c236d82fc405128800d/html5/thumbnails/11.jpg)
EarthScienceExample
• Understandrelationshipsbetweenecosystemdynamics andclimaticfactors
• Modelasaregressionanalysisproblem• 3sciencequestions– Magnitudeandextentofecosystemexposure,sensitivityandresiliencetothe2005and2010Amazondroughts
– Understandhuman-inducedandotherattributionascausesofvegetationanomalies
– Howlearneddependencymodelvariesacrosseco-climaticzonesandgeographicalregionsonaglobalscale
NASAESTOAIST-14project,UncoveringEffectsofClimateVariablesonGlobalVegetation(PI:Kamalika Das,Ph.D.)
![Page 12: NASA Ames Data Sciences Group - Amazon Web Services · final missed clred msl intercept vectored sight gar ... • Given appropriate historical data, have the ability to predict:](https://reader034.fdocuments.us/reader034/viewer/2022050519/5fa28c236d82fc405128800d/html5/thumbnails/12.jpg)
ProblemFormulation
• Point-to-pointregressionanalysis(GeneticProgrammingbasedSymbolicRegression)
• Estimatespatio-temporaldependencyofforestecosystemsonclimatevariables
Vijt=f(Lcij, CVij
t, CVnbt, CVij
t-1, CVnbt-1,.....CVij
t-k, CVnbt-k)
V:vegetation,LC:landcover type,CV:climate variable(s)
i,j:pixellocationindicest:timeindexnb:spatialneighborhoodof
indexi,jk:temporaldependencyOpenchallenges: 1.Estimatingfunctionf
2.Estimatingbestchoicesfork,nb
![Page 13: NASA Ames Data Sciences Group - Amazon Web Services · final missed clred msl intercept vectored sight gar ... • Given appropriate historical data, have the ability to predict:](https://reader034.fdocuments.us/reader034/viewer/2022050519/5fa28c236d82fc405128800d/html5/thumbnails/13.jpg)
DataPipeline
NDVIResolution: 250 m
Projection: Sinusoidal
LSTResolution: 1 km
Projection: Sinusoidal
TRMM (Ver 6)Resolution: 25 kmProjection: WGS84
Reprojectand
resampledata
2000 – 2010 Monthly data
NDVI, TRMM, LST
Resolution: 1 kmProjection: WGS84
Time-Series:Changetoseasonal
2000 – 2010 Seasonal data
Monthly -> Seasonal
4 Seasons/yea
r
Season 1: March – MaySeason 2: June – SepSeason 3: OctSeason 4: Nov - Feb
Windowing:Smoothingover
25x25sizewindow
Filterdatabasedonlandcover
![Page 14: NASA Ames Data Sciences Group - Amazon Web Services · final missed clred msl intercept vectored sight gar ... • Given appropriate historical data, have the ability to predict:](https://reader034.fdocuments.us/reader034/viewer/2022050519/5fa28c236d82fc405128800d/html5/thumbnails/14.jpg)
Resultsfor2004-2010
Mean Squared Error
Year RidgeRegression LASSO SVR Symbolic
Regression
2004 0.284 0.284 0.280 0.262
2005 0.289 0.289 0.288 0.278
2006 0.426 0.426 0.430 0.321
2007 0.374 0.374 0.370 0.318
2008 0.308 0.308 0.310 0.336
2009 0.353 0.353 0.360 0.328
2010 0.546 0.547 0.540 0.479
Marcin Szubert,Anuradha Kodali,Sangram Ganguly,Kamalika Das,andJoshC.Bongard,ReducingAntagonismbetweenBehavioralDiversityandFitnessinSemanticGeneticProgramming,ProceedingsoftheGeneticandEvolutionaryComputation
Conference(GECCO),pp.797-804,2016.
![Page 15: NASA Ames Data Sciences Group - Amazon Web Services · final missed clred msl intercept vectored sight gar ... • Given appropriate historical data, have the ability to predict:](https://reader034.fdocuments.us/reader034/viewer/2022050519/5fa28c236d82fc405128800d/html5/thumbnails/15.jpg)
OngoingandFutureWork• Experimentwithdifferentcombinationsoftemporal
lookback and/orspatialeffects• Introduceadditionalregressors(radiation,forestfiremaps,
deforestationmaps)• StudytheeffectofdifferentregressorsondifferentAmazon
tiles• DerivenonlinearGPmodelsonAmazontiles• Givenappropriatehistoricaldata,havetheabilitytopredict:
“Underwhatconditionsdoesvegetationnotrecoverwithinacertaintimeframe.”
• Doglobalscaleanalysisinparallel
![Page 16: NASA Ames Data Sciences Group - Amazon Web Services · final missed clred msl intercept vectored sight gar ... • Given appropriate historical data, have the ability to predict:](https://reader034.fdocuments.us/reader034/viewer/2022050519/5fa28c236d82fc405128800d/html5/thumbnails/16.jpg)
VESsel GENeration (VESGEN)AnalysisPatriciaParsons-Wingerter,PhD,NASAChiefInnovator/POCNASAAmes2016InnovationFundAward,ChiefTechnologist’sOffice
• VESGEN2Dmapsandquantifiesvascularremodelingforawidevarietyofquasi-2Dvascularizedbiomedicaltissueapplications.
• WorkingontransformingtoVESGEN3D,inlinewithmostvascularizedorgansandtissuesinhumansandvertebrates.
• Vascular-dependentdiseasesincludecancer,diabetes,coronaryvesseldisease,andmajorastronauthealthchallengesinthespacemicrogravityandradiationenvironments,especiallyforlong-durationmissions.
• Onekeycomponentisbinarization:conversionofgrayscaleimagestoblack/whitevascularbranchingpatterns.– Takes10-25hoursofhumaneffort.– Exploringpatternrecognition,matchingfiltering,vessel
tracking/tracing,mathematicalmorphology,multiscaleapproaches,andmodelbasedalgorithms.
![Page 17: NASA Ames Data Sciences Group - Amazon Web Services · final missed clred msl intercept vectored sight gar ... • Given appropriate historical data, have the ability to predict:](https://reader034.fdocuments.us/reader034/viewer/2022050519/5fa28c236d82fc405128800d/html5/thumbnails/17.jpg)
OTSUThresholding
![Page 18: NASA Ames Data Sciences Group - Amazon Web Services · final missed clred msl intercept vectored sight gar ... • Given appropriate historical data, have the ability to predict:](https://reader034.fdocuments.us/reader034/viewer/2022050519/5fa28c236d82fc405128800d/html5/thumbnails/18.jpg)
OTSUvs.AdaptiveThresholding
![Page 19: NASA Ames Data Sciences Group - Amazon Web Services · final missed clred msl intercept vectored sight gar ... • Given appropriate historical data, have the ability to predict:](https://reader034.fdocuments.us/reader034/viewer/2022050519/5fa28c236d82fc405128800d/html5/thumbnails/19.jpg)
FutureWork• Workinprogress:exploringmorepreprocessingandpost-processingtechniques
• Eachstepofpreprocessingandpostprocessing hassomeinputparameters– Theresultissensitivetothisparameters– Weaimtomaketheparameterselectioneitherautomated(machinelearning)orsemi-automated(usercanchoosetherightparameter)
• MachineLearningtolearnthebinarization– Giventhemanuallabels,performsupervisedorsemi-supervisedlearning
– Eachpixelanditsclasslabel(foregroundorbackground)isthetrainingexample
![Page 20: NASA Ames Data Sciences Group - Amazon Web Services · final missed clred msl intercept vectored sight gar ... • Given appropriate historical data, have the ability to predict:](https://reader034.fdocuments.us/reader034/viewer/2022050519/5fa28c236d82fc405128800d/html5/thumbnails/20.jpg)
How dowegettheWordOut?DASHlink
disseminate.collaborate.innovate.https://dashlink.ndc.nasa.gov/
DASHlinkisacollaborativewebsitedesignedtopromote:• Sustainability• Reproducibility• Dissemination• Communitybuilding
Userscancreateprofiles• Sharepapers,uploadanddownloadopensourcealgorithms• FindNASAdatasets.