A New Optical Sensing Device for Real-Time Noncontact Vibration Measurement Considering Light Field Variation
Bibliographic record
Abstract
Vibration measurement is essential for vibration monitoring and control. Noncontact vibration measurement is more applicable in practice than the contact measuring manner, but the noncontact methods usually pose a high requirement in the light field environment. To solve this issue, a new binocular vision method is proposed to enable noncontact vibration measurement with different light fields. In this new method, the multitarget objects in the same image are first recognized by a YOLOv5 model to generate the bounding boxes; meanwhile, a depth image is generated through binocular vision and kept synchronized with the target image. Then, based on each bounding box and its corresponding depth image, an optimal depth value decision algorithm is developed to determine the 3-D real-time coordinates of each object. As a result, the vibration of multitarget objects can be measured simultaneously. An experimental test system was built to evaluate the performance of the proposed method in indoor and outdoor light fields. The tests demonstrate accurate vibration measuring results, and the proposed noncontact method is able to detect very low-frequency vibrations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".