Research of a new sensor method for predicting arbitrary waves through structural configuration analysis
Bibliographic record
Abstract
In this article, based on our understanding of sinusoidal acoustic wave loads identification on the beam foundation acoustic sensor, we extend a new approach to identify arbitrary waves of different amplitudes and waveforms. Since in our new cases study, due to the complexity of moving arbitrary wave loads, the conventional Tikhonov combined with the L -curve method is not an effective way to find the suitable regularization parameter used for wave inverse solutions, we use the Arnoldi–Tikhonov regularization method coupled with the generalized cross validation for seeking regularization parameters; this method proved to be better to find the regularization parameter than the L-curve method. In addition, we study displacement response sensitivities of sensor design parameters, such as geometries of the sensor to optimize the design of the sensor. Meanwhile, we also design a sandwich composite beam sensor to replace the structure with traditional materials, and wave reconstruction results are surprisingly good, even in background noise interference level as high as 20%. Therefore, the performance of the new sensor model is enhanced.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".