Ensuring safety for sampled data systems: An efficient algorithm for filtering potentially unsafe input signals
Why this work is in the frame
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Bibliographic record
Abstract
A common design pattern in cyber-physical systems features a continuous plant and a discrete controller in a feedback loop. Sampled data analysis attempts to take into consideration both the continuous and discrete time elements of such a design. In this paper we adapt an earlier algorithm for efficient ellipsoidal approximation of robust sampled data finite horizon viability kernels to compute capture basins for systems with linear dynamics. Using these capture basins, we construct a hybrid automaton which can verify and if necessary modify an exogenous input signal to ensure safety. The hybrid automaton can be run online in the controller so that it can handle exogenous input signals arriving in real time, such as might be generated by human-in-the-loop control. The technique is demonstrated on a six dimensional nonlinear longitudinal model of a quadrotor with a human pilot in the loop. The capture basins' robustness is used to handle the model nonlinearity in a sound fashion.
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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 | 0.001 | 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