Vibration frequency measurement by DNA-shaped metamaterial array
Bibliographic record
Abstract
Detecting the vibration frequency is a fundamental task of vibration testing. Constrained by the working principle relying on the electrical signal, the current vibration measurement techniques are either expensive, time-consuming, not portable, or susceptible to electromagnetic interference. This research proposes a novel vision-based vibration frequency measurement technique by introducing a mechanical metamaterial array. The array is consisting of a certain number of DNA-shaped metamaterials. Due to the flexible design of metamaterials, the design parameters of different DNA metamaterials can be adjusted to achieve their distinct vibration patterns under certain excitation frequencies, especially the different rotating angles. As a result, when the metamaterial array is attached to a vibrating object, the vibration frequency from the target can be estimated by comparing the vibration patterns of different individual metamaterials. The design of the DNA-shaped metamaterial is first optimized by finite element analysis (FEA) to achieve a significant rotation angle during vibration. Selective laser sintering (SLS) 3D printing is used to create finalized DNA-shaped metamaterials that are subsequently assembled into array structures. The persistence of vision technology is utilized to provide a clear view of the rotational motions. Frequency visualization is achieved through time-lapse photography experiments along with a new data processing algorism to resolve the frequency from rotating motions of the metamaterials array. The simulation and experiment results demonstrate the feasibility and efficiency of the proposed methodology for rapid, noncontact, wireless detection of the vibration frequency of an object.
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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 | 0.000 |
| 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) | 0.001 | 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 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".