{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T17:41:00Z","timestamp":1776706860732,"version":"3.51.2"},"reference-count":34,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T00:00:00Z","timestamp":1564099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China","award":["61571014"],"award-info":[{"award-number":["61571014"]}]},{"name":"Beijing Nature Science Foundation","award":["4172017"],"award-info":[{"award-number":["4172017"]}]},{"name":"The Fundamental Research Funds for Beijing Universities","award":["110052971803\/024"],"award-info":[{"award-number":["110052971803\/024"]}]},{"name":"The Fundamental Research Funds for Beijing Universities","award":["110052971803\/085"],"award-info":[{"award-number":["110052971803\/085"]}]},{"name":"The Fundamental Research Funds for Beijing Universities","award":["110052971921\/015"],"award-info":[{"award-number":["110052971921\/015"]}]},{"name":"Youth Key Talents Project of Organizational Department of Beijing Municipal Committee","award":["401053761856"],"award-info":[{"award-number":["401053761856"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Optical fiber pre-warning systems (OFPS) based on \u03a6-OTDR are applied to many different scenarios such as oil and gas pipeline protection. The recognition of fiber vibration signals is one of the most important parts of this system. According to the characteristics of small sample set, we choose stochastic configuration network (SCN) for recognition. However, due to the interference of environmental and mechanical noise, the recognition effect of vibration signals will be affected. In order to study the effect of noise on signal recognition performance, we recognize noisy optical fiber vibration signals, which superimposed analog white Gaussian noise, white uniform noise, Rayleigh distributed noise, and exponentially distributed noise. Meanwhile, bootstrap sampling (bagging) and AdaBoost ensemble learning methods are combined with original SCN, and Bootstrap-SCN, AdaBoost-SCN, and AdaBoost-Bootstrap-SCN are proposed and compared for noisy signals recognition. Results show that: (1) the recognition rates of two classifiers combined with AdaBoost are higher than the other two methods over the entire noise range; (2) the recognition for noisy signals of AdaBoost-Bootstrap-SCN is better than other methods in recognition of noisy signals.<\/jats:p>","DOI":"10.3390\/s19153293","type":"journal-article","created":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T08:45:39Z","timestamp":1564130739000},"page":"3293","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Ensemble Learning with Stochastic Configuration Network for Noisy Optical Fiber Vibration Signal Recognition"],"prefix":"10.3390","volume":"19","author":[{"given":"Hongquan","family":"Qu","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing 100144, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7910-6048","authenticated-orcid":false,"given":"Tingliang","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing 100144, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing 100144, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanping","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing 100144, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1710","DOI":"10.1016\/j.jngse.2015.10.036","article-title":"The early-warning model of equipment chain in gas pipeline based on DNN-HMM","volume":"27","author":"Qiu","year":"2015","journal-title":"J. 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