Bibliographic record
Abstract
In recent years, fiber optic smart structure is widely studied because of its intrinsic benefits [1] [2]. One of the most striking evolvements is the technology, of which FBG (Fiber Bragg Grating) sensor systems are embedded in fiber reinforced composite materials, so that any ambience induced responses of the host structures can be monitored in real time, consequently proper actuations are initiated. Thus a neural system is realized. This work reports an entire optimization process of the FBG sensors embedded in Graphite/Epoxy composite material. Moreover, performance of the sensor system was observed and the applicability is discussed. Due to the mechanically orthotropic characteristics of the Graphite/Epoxy composite material, two FBG sensors were orthogonally embedded along the two principle axes in mid-plane of the specimen. When strain load was applied along one of the axes, longitudinal and shear responses of the structure were simultaneously monitored, hence its orthotropic properties were determined. Further, any randomly oriented strain applied to the specimen will be analytically quantified along the two sensors. Recurring to surface mounted resistance strain gage concept, the embedded FBG strain gage array was recalibrated, and its sensing alterability is quantified. This work tends to provide a quantitative discussion on FBG sensors’ residual erroneousness after an optimized embedment, the conclusion may give designers a reference to properly interpret FBG sensors’ performances, in case they are used as an embedded strain gage.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".