COMPARISON OF TWO AUTO-TUNING METHODS FOR A VARIABLE STIFFNESS VIBRATION ABSORBER
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Bibliographic record
Abstract
A tunable vibration absorber is developed and its stiffness can be varied on-line. The absorber system is mounted on a clamped-clamped beam acting as a primary system. The objective is to suppress vibration of the primary beam subject to a harmonic excitation whose frequency may vary. A system modeling is conducted. The frequency response of the system is given to show the operating range of the absorber system. Using a simplified two-degree-of-freedom model, two auto-tuning methods are studied. The methods differ in the way of how to identify the exciting frequency. The first method follows a common practice that uses the frequency of the maximum peak in the response spectrum as the exciting frequency. The second method makes use of information of both the response spectrum and the natural frequencies. An experimental study is conducted to compare the two methods. The study has shown that the second method performs better than the first method in terms of frequency tracking ability and robustness to disturbance.
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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 it