Vision-Based Estimation of Cable Slab Forces in Precision Motion Applications
Bibliographic record
Abstract
In this study, a novel method for estimating the cable slab internal and reaction forces is presented. Using visual feedback comprising a high-speed camera, the cable slab responses due to cyclic and acyclic motion profiles are recorded, and the positions of a sufficient finite set of markers attached to the cable slab are extracted off-line using image processing. The kinematics of the markers are obtained via numerical differentiation and signal processing. Adopting the Voigt viscoelastic model, the cable slab is segmented into several lumped mass-spring-damper elements whose linear and angular motions are expressed analytically. Using the strain and its rate of change during motion, estimates of internal and reaction forces are written as functions of the cable slab composite material properties that are identified by manual tuning. The link between these forces and the motion system kinematics reveals the tuning process of a proposed fixed-parameter feedforward/feedback controller/compensator that can be used to enhance the precision of the motion system despite its simplicity. The experimental results reveal the effectiveness of the proposed approach.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".