Bibliographic record
Abstract
Based on the study of the sensing property of fiber bragg grating (FBG) sensor network, this paper takes hybrid wavelength division/time division multiplexing sensor network with high-capacity FBG as the research object to study noise source, noise category and traditional noise reduction algorithm. In accordance with the characteristic of crosstalk accumulation noise in the network, this paper proposes LCEEMD-LWT signal processing method, which uses the local complementary ensemble empirical mean decomposition (LCEEMD) method for signal preprocessing, and then adopts LWT technology to refine high-frequency signal and solve the related problems. Then this paper put forwards detrended fluctuation analysis (DFA) to evaluate the sensing signal of high-capacity FBG, preprocesses the sensing signal by LCEEWD-LWT method, and conducts a temperature sensing experiment. The average temperature error of the demodulating system is reduced from 0.2923℃ to 0.2357℃, which not only overcomes the shortcoming that the traditional signal processing method is not meticulous for high-frequency signal processing, but also avoids the problem that the signal processing time is too long, exerting a good effect on the signal pre-processing of high-capacity FBG. The method in this paper has certain theoretical and technical reference value for the signal processing and the improvement of sensing property of high-capacity FBG sensing demodulating system in engineering application.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".