Data-Driven Approach for Generating Tricomponent Nonstationary Non-Gaussian Thunderstorm Wind Records Using Continuous Wavelet Transforms and S-Transform
Bibliographic record
Abstract
Strong thunderstorm winds cause damage to structures. However, the available number of the tricomponent thunderstorm wind record with a subsecond sampling time interval is limited. In the present study, a record-based procedure for generating tricomponent nonstationary non-Gaussian thunderstorm wind records was proposed. The procedure was based on the iterative power and amplitude correction algorithm framework but with modifications. The modifications were aimed at increasing the variability of the sampled record components by randomizing the power spectral density functions of processes through a digital filter in the frequency domain and improving the convergence by using a relaxation factor for the synchronized phase shift. The formulation and algorithm for the proposed procedure were given by considering the continuous wavelet transform with the harmonic wavelet and generalized Morse wavelet, and the generalized S-transform, which can provide good time localized resolution at high frequencies (low scales) and good resolution at low frequencies (high scales) simultaneously. The proposed procedure, unlike some of the algorithms available in the literature, matches the marginal mixture cumulative distributions of the seed record components and does not require the separation of low- and high-frequency wind components. The use of the proposed procedure to sample tricomponent thunderstorm wind records was shown.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".