Efficient reliability computation of consecutive-k-out-of-n: F systems with shared components
Bibliographic record
Abstract
Consecutive- k systems have been studied extensively in reliability engineering. Linear and circular consecutive- k-out-of- n: F systems with shared component(s) have been studied recently by Lin et al. and Yin and Cui . They considered two adjacent subsystems overlapping with one (multiple) shared component(s), respectively, and obtained system reliability formulas by summing the reliability values for all disjoint cases. As their method is computationally intensive, it would be of interest to develop a simpler and more efficient method for the computation of the reliability function of such systems instead of requiring to list all disjoint cases. In this work, by employing the finite Markov chain imbedding approach, we develop unified formulas as products of matrices for evaluating system reliabilities by redefining the state space of the Markov chain. The results developed here decrease the complexity in the computation of system reliability. Furthermore, the new method is also employed to obtain reliability formulas for Markov-dependent cases. A case study of communication systems is finally presented and some numerical examples are presented to illustrate the developed model and the corresponding results.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".