Recent progress in optical fiber sensors based on Brillouin scattering at university of Ottawa
Bibliographic record
Abstract
The distributed sensor is proven to be a powerful tool for civil structural and material process monitoring. Brillouin scattering in fiber can be used as point sensors or distributed sensors for measurement of temperature, strain, birefringence and vibration over centimeters (Brillouin grating length) for point sensor or the pulse length for the distributed sensor. Simultaneous strain and temperature measurement with a spatial resolution of 20 cm is demonstrated in a Panda fiber using Brillouin grating technique with the temperature accuracy and strain accuracy of 0.4 °C and 9 μɛ. This technique can also be used for distributed birefringence measurement. For Brillouin optical time domain analysis (BOTDA), we have developed a new technique to measure differential Brillouin gain instead of Brillouin gain itself. This technique allows high precision temperature and strain measurement over long sensing length with sub-meter spatial resolution: 50-cm spatial resolution for 50-km length, using return-to-zero coded optical pulses of BOTDA with the temperature resolution of 0.7 °C, which is equivalent to strain accuracy of 12 μɛ. For over 50-km sensing length, we proposed and demonstrated frequency-division-multiplexing (FDM) and time-division-multiplexing (TDM) based BOTDA technique for 75-km and 100-km sensing length without inline amplification within the sensing length. The spatial resolution of 2 m (100 km) and Brillouin frequency shift accuracy of 1.5 MHz have been obtained for TDM based BOTDA and 1-m resolution (75 km) with Brillouin frequency shift accuracy of 1 MHz using FDM based BOTDA. The civil structural health monitoring with BOTDA technique has been demonstrated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".