A Robust Asymmetric Kernel Function for Bayesian Optimization, With Application to Image Defect Detection in Manufacturing Systems

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dc.contributor.authorAlBahar, Areejen
dc.contributor.authorKim, Inyoungen
dc.contributor.authorYue, Xiaoweien
dc.date.accessioned2022-02-05T15:39:23Zen
dc.date.available2022-02-05T15:39:23Zen
dc.date.issued2021-09-29en
dc.date.updated2022-02-05T15:39:21Zen
dc.description.abstractSome response surface functions in complex engineering systems are usually highly nonlinear, unformed, and expensive to evaluate. To tackle this challenge, Bayesian optimization (BO), which conducts sequential design via a posterior distribution over the objective function, is a critical method used to find the global optimum of black-box functions. Kernel functions play an important role in shaping the posterior distribution of the estimated function. The widely used kernel function, e.g., radial basis function (RBF), is very vulnerable and susceptible to outliers; the existence of outliers is causing its Gaussian process (GP) surrogate model to be sporadic. In this article, we propose a robust kernel function, asymmetric elastic net radial basis function (AEN-RBF). Its validity as a kernel function and computational complexity are evaluated. When compared with the baseline RBF kernel, we prove theoretically that AEN-RBF can realize smaller mean squared prediction error under mild conditions. The proposed AEN-RBF kernel function can also realize faster convergence to the global optimum. We also show that the AEN-RBF kernel function is less sensitive to outliers, and hence improves the robustness of the corresponding BO with GPs. Through extensive evaluations carried out on synthetic and real-world optimization problems, we show that AEN-RBF outperforms the existing benchmark kernel functions.en
dc.description.versionAccepted versionen
dc.format.extent12 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/TASE.2021.3114157en
dc.identifier.eissn1558-3783en
dc.identifier.issn1545-5955en
dc.identifier.issue99en
dc.identifier.orcidYue, Xiaowei [0000-0001-6019-0940]en
dc.identifier.urihttp://hdl.handle.net/10919/108145en
dc.identifier.volumePPen
dc.language.isoenen
dc.publisherIEEEen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000732172900001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectAutomation & Control Systemsen
dc.subjectKernelen
dc.subjectOptimizationen
dc.subjectLinear programmingen
dc.subjectModelingen
dc.subjectBayes methodsen
dc.subjectProbabilistic logicen
dc.subjectComputational modelingen
dc.subjectAdvanced manufacturingen
dc.subjectBayesian optimization (BO)en
dc.subjectdefect detectionen
dc.subjectGaussian process (GP)en
dc.subjectprocess optimizationen
dc.subjectGAUSSIAN-PROCESSESen
dc.subjectSELECTIONen
dc.subjectIndustrial Engineering & Automationen
dc.subject0906 Electrical and Electronic Engineeringen
dc.subject0910 Manufacturing Engineeringen
dc.subject0913 Mechanical Engineeringen
dc.titleA Robust Asymmetric Kernel Function for Bayesian Optimization, With Application to Image Defect Detection in Manufacturing Systemsen
dc.title.serialIEEE Transactions on Automation Science and Engineeringen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherEarly Accessen
dc.type.otherJournalen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Industrial and Systems Engineeringen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen

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