High Accuracy Vibration Sensing by using Random Interval Forest Classifier Based Distributed Fiber Sensing

Brian Pamukti, Wang Zi, Shien Kuei Liaw*, Ya Mei Yang, Totok Soehartanto, Agus Muhamad Hatta

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present a novel machine-learning technique for vibration detection, called random interval forest classifier (RIFC), using a single Mach-Zehnder interferometer based on single-mode fiber. Our method achieved 98% accuracy with very low complexity.

Original languageEnglish
Title of host publication16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350372076
DOIs
Publication statusPublished - 2024
Event16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024 - Incheon, Korea, Republic of
Duration: 4 Aug 20249 Aug 2024

Publication series

Name16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

Conference

Conference16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024
Country/TerritoryKorea, Republic of
CityIncheon
Period4/08/249/08/24

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