Sensor uncertainty management for an encapsulated logical device architecture. Part II: a control policy for sensor uncertainty
Bibliographic record
Abstract
For Part I see ACC, Arlington, VA. USA (2001). A procedure to perform data fusion inside a low-level control loop was developed and implemented on a 1-DOF manipulator. This procedure uses sensory data provided by low-level and non-dedicated high-level sensors, at different rates. Fusion of the multiple feedback signals generates a signal with a smaller uncertainty level. The performance of the control scheme is directly related to the quality and relevance of the feedback signal. In this control policy, data fusion is performed with the data coming from the different sensors, once they have been time-correlated using Kalman filters. Also, In order to stabilize the fused feedback signal when there is no data available from the slower sensors, a Kalman filter is used to observe and generate a prediction of the fused measurement signal, which can then be used by the data fusion process. The slow sensor processing delay compensation and fused measurement stabilization are independent of the fusion process. Therefore, any data fusion process can be used with this procedure, as long as the process respects the real-time constraint of the low-level control loop.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".