利用DBSCAN优化船舶领域算法的实时碰撞预警模型-豆柴文库

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利用DBSCAN优化船舶领域算法的实时碰撞预警模型
Title:OptimizationofReal-timeCollisionWarningModelintheMaritimeDomainusingDBSCAN
Abstract:
Inthemaritimedomain,thepreventionofcollisionsbetweenshipsisofparamountimportancetoensurethesafetyofcrew,passengers,andcargo.ThispaperproposestheutilizationofDensity-BasedSpatialClusteringofApplicationswithNoise(DBSCAN)algorithmtooptimizereal-timecollisionwarningmodelsinthemaritimedomain.ByemployingDBSCAN,themodelcaneffectivelyhandlethedenseandnoisyshipdata,accuratelyidentifypotentialcollisionthreats,andprovidetimelywarnings.Theexperimentalresultsdemonstratetheeffectivenessandefficiencyoftheproposedapproachinenhancingcollisiondetectionandimprovingmaritimesafety.
1.Introduction
Theincreaseinmaritimetraffichashighlightedtheneedforeffectivecollisionwarningsystemstopreventaccidentsatsea.Traditionalcollisionwarningmodelsbasedonsimplegeometricrulesoftenfailtoconsidertheinherentcomplexitiesanddynamicspresentinshiptrafficpatterns.TheDBSCANalgorithm,knownforitsabilitytocopewithnoiseanddensityvariations,offersapromisingsolutiontooptimizethepredictionaccuracyandreducefalsealarmsincollisionwarningsystems.
2.Background
2.1ShipTrafficPatternAnalysis
Understandingshiptrafficpatternsiscrucialforpredictingcollisionthreatsaccurately.Analyzingshiptrajectories,speed,andcourseinformationhelpsinidentifyingcriticalareaspronetocongestionandpotentialcollisionrisks.
2.2DBSCANAlgorithm
DBSCAN,adensity-basedclusteringalgorithm,groupsdatapointsbasedontheirspatialdensity.Itovercomesthelimitationsoftraditionalclusteringalgorithmsandiscapableofhandlingnoiseandvaryingdensityregionseffectively.
3.Methodology
Theproposedoptimizationofthereal-timecollisionwarningmodelinthemaritimedomaininvolvesthefollowingsteps:
3.1DataPreprocessingandFeatureExtraction
Rawshiptrajectorydata,includingposition,speed,andcourse,arecollectedandpreprocessedtoremovenoise,outliers,andredundantinformation.Relevantfeatures,suchasrelativespeed,distance,andpossiblecollisionriskindicators,areextracted.
3.2DBSCAN-Base