Semiautomatic Model-Based View Planning for Active Triangulation 3-D Inspection Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A semiautomatic model-based approach to the view planning problem for high-resolution active triangulation 3-D inspection systems is presented. First, a comprehensive, general, high-fidelity model of such systems is developed for the evaluation of configurations with respect to a model of task requirements, with a bounded scalar performance metric. The design process is analyzed, and the automated view planning problem is formulated only for the critically difficult aspects of design. A particle swarm optimization algorithm is applied to the latter portion, including probabilistic modeling of positioning error, using the performance metric as an objective function. The process leverages human strengths for the high-level design, refines low-level details mechanically, and provides an absolute measure of task-specific performance of the resulting design specification. The system model is validated, allowing for a reliable rapid design cycle entirely in simulation. Parameterization of the optimization algorithm is analyzed and explored empirically for performance.
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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