Multilevel decomposition of vibration signals from motorcycles using the wavelet–Hilbert transformation
Bibliographic record
Abstract
To maximize comfort and safety it is important to control motorcycle vibration. In order to assess the vibration of motorcycles, measurements need to be taken from these machines. Vibration signals acquired using sensors mounted on the machine always include the excitation components from the engine, the road surface, and the structure of the motorcycle frame. Generally, these vibration signals are coupled together. Presently, there is no ideal method to separate these signal components to allow better analysis of the different contributing factors. Contributions to the total vibration signal from the main vibration sources (the engine and the road surface) cannot be determined exactly. To solve this problem, a multilevel decomposition algorithm based on the wavelet–Hilbert transform is proposed in this paper. The paper formulates the vibration signal model for a motorcycle and discusses the performance of the multilevel decomposition algorithm using simulation and experimental methods. The simulation and experimental results show that the proposed new algorithm is an effective method for the identification of vibration sources on a motorcycle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".