Performance of Hybrid Clustering-Classification Approach for Dual-Band System in a Mode-Locked Fiber Laser

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dc.contributor.authorGutierrez C.D.
dc.contributor.authorRuiz J.N.
dc.contributor.authorSalazar S.C.
dc.contributor.authorLopez J.P.G.
dc.contributor.authorZapata J.D.
dc.contributor.authorBotia J.F.
dc.date.accessioned2024-09-01T06:17:10Z
dc.date.available2024-09-01T06:17:10Z
dc.date.issued2024
dc.description.abstract© 2013 IEEE.This paper presents the performance results of a hybrid machine-learning model in the task of classifying light pulse spectra in the regimes (modes) of the following optical system: mode-locked in Erbium-doped fiber laser using nonlinear polarization rotation based on monolayer graphene. The four modes studied are continuous waves, pulses at wavelengths 1533 and 1555~nm , and Dual-Band (both wavelengths). The model is a mix of an unsupervised process for identifying pulses at 1533~nm and a supervised process for characterizing the remaining modes. The algorithms used are K-means, for the unsupervised stage, and Light Gradient Boosting Machine for supervised learning. Performance is mainly reported by using balanced accuracy, where the model reached 88% compared to manual classification techniques. We also tested the classification speed of our model regarding manual process. We found an average computing time of 10.8 ms for the trained model whereas former technique time was around three orders of magnitude above. This represents a huge improving in time consumption in classification.
dc.description.firstpage104115
dc.description.lastpage104125
dc.description.volume12
dc.identifier.doi10.1109/ACCESS.2024.3409565
dc.identifier.issnNone
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/39289
dc.relation.ispartofIEEE Access
dc.rightsAcesso Aberto
dc.subject.otherlanguageComputational processing time
dc.subject.otherlanguagedual-band
dc.subject.otherlanguagehybrid machine learning model
dc.subject.otherlanguagemode-locked lasers
dc.titlePerformance of Hybrid Clustering-Classification Approach for Dual-Band System in a Mode-Locked Fiber Laser
dc.typeArtigo
local.scopus.citations0
local.scopus.eid2-s2.0-85195389632
local.scopus.subjectClassification algorithm
local.scopus.subjectComputational processing time
local.scopus.subjectDual Band
local.scopus.subjectHybrid machine learning
local.scopus.subjectHybrid machine learning model
local.scopus.subjectLaser mode-locking
local.scopus.subjectMachine learning models
local.scopus.subjectModelocked lasers
local.scopus.subjectPerformance
local.scopus.subjectUltrafast optics
local.scopus.updated2025-04-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85195389632&origin=inward
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