Wavelet Transform to Index Road Vehicle Vibration Mixed Mode Signals
Bibliographic record
Abstract
Goods and products transported by road are subjected to vehicle vibration which, without proper protective packaging, can suffer damage. To reduce shipment costs, protection has to be optimised to limit product damage occurrence while keeping packaging weight and size to a minimum. Optimisation is realized by simulating the vibration of transport vehicles. To achieve an accurate simulation, each vehicle vibration mode has to be modelled. These include: the nonstationary random vibration induced by road roughness and speed variations, the transient vibration created by road surface aberrations and the harmonic vibration created by the vehicle engine and drive train. Identifying and indexing these mixed-modes within complex road vehicle vibration signals is essential to define the severity and occurrence of the different modes in order to develop an accurate model. This paper shows that indexing can be performed using the orthogonal wavelet transform such as Daubechies 10. Assuming that each mode is preponderant in different analysis scales, the wavelet coefficients can be used to perform the indexing. This allows more sensitive mode detection and a more precise time indexing thanks to the multi-resolution nature of the wavelet transform compared to other time-frequency analysis methods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".