A New Availability Assessment Method for Complex Control Systems With Multi-Characteristics
Bibliographic record
Abstract
This paper proposes a new availability assessment method for complex control systems with multi-characteristics based on the goal-oriented (GO) method to evaluate the lower confidence limit of its system availability. First, the GO method for complex control systems with multi-characteristics is illustrated in terms of expounding GO operators and proposing a new exact algorithm with shared signals. Then, the new availability assessment method is expounded in detail, and its process is formulated. Finally, the new method is used to evaluate the lower confidence limit of the system stability of the electrohydraulic control system of a power-shift steering transmission as an example. To verify its advantages and rationality, the availability assessment results and evaluation efficiency are compared with those obtained by the regular Monte Carlo method and the regular exact GO algorithm with shared signals. Furthermore, the coverage rate of the lower confidence limit of the system availability obtained by the new method is compared with the nominal significance level. Overall, this availability assessment method not only improves the theory of the GO method and widens its application but also provides a new approach for the availability assessment of complex control systems that reduces costs and improves the estimation efficiency and accuracy.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".