RUcore: Rutgers University Community Repository
Bayesian optimization for monitoring the dynamic environment
Descriptive
TitleInfo
Title
Bayesian optimization for monitoring the dynamic environment
Name
(type = personal)
NamePart
(type = family)
Gao
NamePart
(type = given)
Tianyu
NamePart
(type = date)
1993-
DisplayForm
Tianyu Gao
Role
RoleTerm
(authority = RULIB)
author
Name
(type = personal)
NamePart
(type = family)
Bai
NamePart
(type = given)
Xiaoli
DisplayForm
Xiaoli Bai
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
chair
Name
(type = personal)
NamePart
(type = family)
Burlion
NamePart
(type = given)
Laurent
DisplayForm
Laurent Burlion
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
internal member
Name
(type = personal)
NamePart
(type = family)
Zou
NamePart
(type = given)
Qingze
DisplayForm
Qingze Zou
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
internal member
Name
(type = corporate)
NamePart
Rutgers University
Role
RoleTerm
(authority = RULIB)
degree grantor
Name
(type = corporate)
NamePart
School of Graduate Studies
Role
RoleTerm
(authority = RULIB)
school
TypeOfResource
Text
Genre
(authority = marcgt)
theses
Genre
(authority = ExL-Esploro)
ETD graduate
OriginInfo
DateCreated
(qualifier = exact);
(encoding = w3cdtf);
(keyDate = yes)
2020
DateOther
(type = degree);
(qualifier = exact);
(encoding = w3cdtf)
2020-10
Language
LanguageTerm
(authority = ISO 639-3:2007);
(type = text)
English
Abstract
(type = abstract)
How to sample the data in an optimization algorithm is important in an environmental monitoring problem. Ensuring the sampling method practical while obtaining useful information as much as possible to reduce time and energy cost during optimization is the key. This thesis focuses on the implementation of Bayesian Optimization (BO) to monitor a time-varying three-dimensional environment. The BO algorithm is based on the Gaussian Processes (GPs) surrogate models which are non-parametric regression methods, and uses the reward function for decision making. An uniquely designed kernal function is used in GPs to learn the underlying pattern of spatial and temporal variations. A seies of theoretical but less practical experiments are developed to prove the capability of BO, together with presenting the importance of temporal information. A continuous path planning is designed to replace the waypoint planning for a real path design in the environmetal monitoring. Furthermore, this planning is effective to balance the trade-off between the exploration and the exploitation in the optimization problem.
Subject
(authority = local)
Topic
Bayesian optimization
Subject
(authority = RUETD)
Topic
Mechanical and Aerospace Engineering
RelatedItem
(type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
(type = RULIB)
ETD
Identifier
ETD_11194
PhysicalDescription
Form
(authority = gmd)
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (vi, 39 pages) : illustrations
Note
(type = degree)
M.S.
Note
(type = bibliography)
Includes bibliographical references
RelatedItem
(type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier
(type = local)
rucore10001600001
Location
PhysicalLocation
(authority = marcorg);
(displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier
(type = doi)
doi:10.7282/t3-fj4k-pr56
Back to the top
Rights
RightsDeclaration
(ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder
(type = personal)
Name
FamilyName
Gao
GivenName
Tianyu
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
(encoding = w3cdtf);
(qualifier = exact);
(point = start)
2020-09-24 02:44:22
AssociatedEntity
Name
Tianyu Gao
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
Back to the top
Technical
RULTechMD
(ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem
(VERSION = 5.1)
windows xp
CreatingApplication
Version
1.5
ApplicationName
MiKTeX pdfTeX-1.40.20
DateCreated
(point = end);
(encoding = w3cdtf);
(qualifier = exact)
2020-10-01T08:54:39
DateCreated
(point = end);
(encoding = w3cdtf);
(qualifier = exact)
2020-10-01T08:54:39
Back to the top
Statistical Profile