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Record W4413745928 · doi:10.1364/jocn.561775

Multi-task localization based on Φ-OTDR: composite vibration recognition, synchronous localization, and co-trench position

2025· article· en· W4413745928 on OpenAlex
Wenxin Liu, Zhiwei Wang, Qiuyan Yao, Mingyuan Wu, Tiankuo Yu, Jie Zhang, Mohamed Cheriet

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Optical Communications and Networking · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversité du QuébecCentrale des Syndicats du QuébecUniversité du Québec à Montréal
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of ChinaState Key Laboratory of Information Photonics and Optical CommunicationsChina Association for Science and Technology
KeywordsPosition (finance)Composite numberVibrationComputer scienceTask (project management)Optical time-domain reflectometerTrenchAcousticsEngineeringOptical fiberTelecommunicationsMaterials sciencePhysicsFiber optic sensorAlgorithm

Abstract

fetched live from OpenAlex

In optical fiber networks, ensuring reliability is crucial as both newly activated and pre-existing associated services encounter co-trenching risks and potential security threats. To address these challenges, we propose a Φ-OTDR-based multi-task localization framework integrating composite vibration event recognition, synchronous localization, and co-trench position detection. Analyzing real-time vibration signals, our method achieves 95.41% event synchronous positioning, 99.50% event classification, and 92.25% co-trench location accuracy, with 98.17% robustness on 400 test samples. These results demonstrate the effectiveness of the proposed framework in enhancing the safety of optical fibers and supporting the stable operation of optical fiber networks.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.932
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.269
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it