Development of the offset-locking-based distributed sensor
Bibliographic record
Abstract
High sensitivity, real time distributed and cost effective sensor system is in great need for structure healthy monitoring in civil engineering. In our lab, we are developing a distributed, Stimulated Brillouin Scattering based, fiber optic sensing system at 1550nm wavelength. Our current SBS-based fiber optic sensor system works at 1310nm wavelength. Two expensive Nd: YAG Lasers (US$40,000 each) are being used, which leads to a soaring high cost to the entire system and eventually limits its application. Distributed Feedback (DFB) lasers have large tenability, compact size and low cost (less than US$1000 each). But they are not stable enough for the sensing system. In this project, we use the frequency offset locking technique with optical delay line and electrical feedback circuit to optimizing the stability of DFB lasers so that the lasers of 1310 nm in the sensor system can be substituted by the lasers of 1550 nm that is the most often used band in modern fiber optic telecommunication system. Less than 100 kHz stability of the beat frequency is required to achieve temperature accuracy of 0.1°C and strain accuracy of 2me. In our system we have realized 20 kHz stability of beat frequency of two DFB lasers. Greater than 800MHz turning range is necessary for the detection of temperature range of 600 °C and strain range of 10,000 me. In our system we have achieved 925 MHz in 18.75 seconds. In the sensing part, we can vary the pulse width from 120ns to 5ns that means we can realize the spatial resolution of 50cm at least. Because the total optical loss in the setup is comparably smaller, the measurable fiber length is mainly determined by the optical power launched to the fiber, normally it is in tens kilometers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".