WAKE DETECTION USING CONVOLUTIONAL NEURAL NETWORKS

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dc.contributor.advisorRadko, Timour
dc.contributor.authorZimny, Jacqueline
dc.contributor.departmentOceanography (OC)
dc.contributor.secondreaderBrown, Justin M.
dc.date.accessioned2022-02-11T00:14:25Z
dc.date.available2022-02-11T00:14:25Z
dc.date.issued2021-12
dc.description.abstractAdvances in engineering and technology have made acoustic detection of submarines increasingly difficult. Using hydrodynamic signatures created by propagating submarines is an alternative method for submarine detection. Detection based on hydrodynamic signatures may also offer unique tactical advantages, given the tendency of wakes to persist for long timescales. Artificial neural networks trained on velocity field data show promise to automate detection. We used numerical simulations to generate velocity data on wake, jet, and convective turbulence. All these forms of turbulence have similar characteristics in the velocity field, yet their spectra reveal subtle differences that could be exploited for wake identification purposes. We then trained a convolutional neural network with the simulation results and demonstrated that neural networks can classify turbulent flows based on small-scale features with high accuracy. In particular, we find that the developed algorithms can successfully identify wakes in 92% of cases, which implies that the AI-based technology is viable and ready for the transition to the analysis of the experimental and field data.en_US
dc.description.distributionstatementApproved for public release. Distribution is unlimited.en_US
dc.description.serviceLieutenant Commander, United States Navyen_US
dc.description.sponsorshipOffice of Naval Research, 875 North Randolph Street, Arlington, VA 22203en_US
dc.identifier.curriculumcode373, Meteorology and Oceanography (METOC)
dc.identifier.thesisid37049
dc.identifier.urihttps://hdl.handle.net/10945/68766
dc.publisherMonterey, CA; Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.subject.authorwakeen_US
dc.subject.authordetectionen_US
dc.subject.authorartificial intelligenceen_US
dc.subject.authorneural networksen_US
dc.subject.authorconvectionen_US
dc.subject.authorjetsen_US
dc.subject.authorturbulenceen_US
dc.titleWAKE DETECTION USING CONVOLUTIONAL NEURAL NETWORKSen_US
dc.typeThesisen_US
dspace.entity.typePublication
etd.thesisdegree.disciplineMeteorology and Physical Oceanographyen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.nameMaster of Science in Meteorology and Physical Oceanographyen_US
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