High-Speed and High-Resolution Interrogation of a Strain and Temperature Random Grating Sensor
Bibliographic record
Abstract
High-speed and high-resolution interrogation of a random fiber grating sensor based on spectral shaping and wavelength-to-time (SS-WTT) mapping, and pulse compression for simultaneous measurement of strain and temperature is proposed and demonstrated. In the proposed system, an ultrashort pulse is spectrum shaped by a high-birefringence (Hi-Bi) random grating (HBRG) to generate two orthogonally polarized spectra with a wavelength difference determined by the birefringence of the HBRG, which are then fed to a dispersive optical loop in which a linearly chirped fiber Bragg grating (LCFBG) is incorporated, to perform linear WTT mapping, to generate two temporally separated optical pulses, which are converted to two random electrical waveforms at a photodetector). Random pulse compression is then performed to increase the interrogation resolution. By measuring the time shifts of the temporally compressed pulses, the strain and temperature information is retrieved. An experiment is performed. The experimental results show that the proposed random grating sensor and its interrogation system can provide a strain and temperature resolution of 7.1 μϵ and 0.79 °C at an ultrahigh speed of 20 MHz.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".