Bibliographic record
Abstract
The major benefit of the kinematic Kalman filter (KKF), i.e., the state estimation based on kinematic model is that it is immune to parameter variations and unknown disturbances regardless of the operating conditions. In carrying out complex motion tasks such as the coordinated manipulation among multiple machines, some of the motion variables measured by sensors may only be available through the communication layer, which requires to formulate the optimal state estimator subject to lossy network. In contrast to standard dynamic systems, the kinematic model used in the KKF relies on sensory data not only for the output but also for the process input. This paper studies how the packet dropout occurring from the input sensor as well as the output sensor affects the performance of the KKF. When the output sensory data are delivered through the lossy network, it has been shown that the mean error covariance of the KKF is bounded for any non-zero packet arrival rate. On the other hand, if the input sensory data are subject to lossy network, the Bernoulli dropout model results in an unbounded mean error covariance. More practical strategy is to adopt the previous input estimate in case the current packet is dropped. For each case of packet dropout models, the stochastic characteristics of the mean error covariance are analyzed and compared. Simulation results are presented to illustrate the analytical results and to compare the performance of the time varying (optimal) filter gain with that of the static (sub-optimal) filter gain.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".