Nested Markov chain — A novel approach to model network-induced constraints
Bibliographic record
Abstract
During the past a few years, control systems are greatly influenced by the evolution of networked control systems (NCSs). However, challenges such as time-delay and packet dropouts in NCSs are still breaking its stride. To analyse and tackle those constraints, Markov chains had been widely used to model time-delay and packet dropouts, and a set of controllers had been designed thereafter. Nevertheless, none of those models considered the relation between those constraints, which will be investigated in this paper. In this proposed system model, nested Markov chain (NMC) with one main Markov chain used to model packet dropouts and two sub-Markov chains responsible for the time-delays is exploited. Furthermore, the difference between Markov chain model and NMC model is discussed in detail. The modeled process and evaluation results illustrate the effectiveness of this NMC model.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".