Evaluating the capability of time-of-flight cameras for accurately imaging a cyclically loaded beam
Bibliographic record
Abstract
Time-of-flight cameras are used for diverse applications ranging from human-machine interfaces and gaming to robotics and earth topography. This paper aims at evaluating the capability of the Mesa Imaging SR4000 and the Microsoft Kinect 2.0 time-of-flight cameras for accurately imaging the top surface of a concrete beam subjected to fatigue loading in laboratory conditions. Whereas previous work has demonstrated the success of such sensors for measuring the response at point locations, the aim here is to measure the entire beam surface in support of the overall objective of evaluating the effectiveness of concrete beam reinforcement with steel fibre reinforced polymer sheets. After applying corrections for lens distortions to the data and differencing images over time to remove systematic errors due to internal scattering, the periodic deflections experienced by the beam have been estimated for the entire top surface of the beam and at witness plates attached. The results have been assessed by comparison with measurements from highly-accurate laser displacement transducers. This study concludes that both the Microsoft Kinect 2.0 and the Mesa Imaging SR4000s are capable of sensing a moving surface with sub-millimeter accuracy once the image distortions have been modeled and removed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".