Title:
Anomaly detection based on the estimation of speed and flow mapping for controlled Lagrangian particles

dc.contributor.advisor Zhang, Fumin
dc.contributor.advisor Edwards, Catherine R.
dc.contributor.author Cho, Sungjin
dc.contributor.committeeMember Vela, Patricio
dc.contributor.committeeMember Ma, Xiaoli
dc.contributor.committeeMember West, Michael
dc.contributor.committeeMember Rogers, Jonathan
dc.contributor.department Electrical and Computer Engineering
dc.date.accessioned 2020-01-14T14:40:30Z
dc.date.available 2020-01-14T14:40:30Z
dc.date.created 2017-12
dc.date.issued 2017-11-13
dc.date.submitted December 2017
dc.date.updated 2020-01-14T14:40:30Z
dc.description.abstract The main contribution of this dissertation is a set of algorithms that detect anomaly of autonomous underwater vehicles (AUVs) without sensors monitoring vehicle components. Only using trajectory information, the proposed strategy detects abnormal vehicle motion under unknown ocean flow. It has the potential for mitigating abnormal vehicle motion with path-planning and controller design of AUVs. The experimental results of the Georgia Tech Miniature Autonomous Blimp (GT-MAB) and Georgia Tech Wind Measuring Robot (GT WMR) in an indoor test bed verify the proposed strategy.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/62180
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Anomaly detection
dc.subject Fault detection
dc.subject Autonomous underwater vehicles
dc.subject Autonomous indoor blimp
dc.subject Acoustic detection
dc.subject Wind field mapping
dc.subject Adaptive control
dc.title Anomaly detection based on the estimation of speed and flow mapping for controlled Lagrangian particles
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Zhang, Fumin
local.contributor.corporatename School of Electrical and Computer Engineering
local.contributor.corporatename College of Engineering
relation.isAdvisorOfPublication 65d2541f-4ce1-40d0-923b-09e66eb45b33
relation.isOrgUnitOfPublication 5b7adef2-447c-4270-b9fc-846bd76f80f2
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level Doctoral
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