Recent advances in structural processing: Resolution of short transients of unknown parameters
Bibliographic record
Abstract
Resolution of transients of identical structures arises in such areas as machinery vibration, seismic and underwater acoustics. These processes are characterized by highly correlated nonstationary nature over a short interval. In extreme case, where the transients have the same structure and the record-length is short, even the most powerful adaptive methods become inefficient. Composite parameter-free modeling (CPFM) [E. I. Plotkin and M. N. S. Swamy, Int. J. Acoust. Vib. 4, 159–164 (1999)] is one of the effective techniques for improving the resolution of closely spaced (in time and frequency) short transients of unknown parameters. The model presented is a nested-form composition of null filters; the inner building blocks (variable-frame matched filters) are used to suppress one of the transients, while the outer nonlinear structure nullifies the second transient. The proposed model permits linear estimation of the target transient corrupted by almost identical targetlike interference. This model exhibits clear advantage in reconstructing a target signal in the presence of a powerful multi-tone transient of unknown parameters. The estimated envelope and phase of the target rapidly converge to their steady-state values, while the conventional approach produces prolonged lags with extensive fluctuations, precluding reliable reconstruction of the target signal. [Work supported by the NSERC.]
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".