OPTIMAL DESIGN OF BINARY WEIGHTED k-OUT-OF-n SYSTEMS
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Bibliographic record
Abstract
In this paper, we consider the optimal design of the binary weighted k-out-of-n system. The binary weighted k-out-of-n: G system works if and only if the total utility of all working components is at least k. In the design process, we need to evaluate system reliability repetitively. The universal generating function (UGF) approach is used for this purpose when the system size is small or moderate. When the size of the system is large, the recursive approach is used, which is more efficient. Two optimal models are formulated. One is to minimize the expected total cost while guaranteeing the system reliability higher than a pre-specified value; the other is to maximize the system reliability with the constraints on total system cost. Genetic algorithms (GA) and Tabu Search (TS) methods are both used to solve the proposed optimization models. Since the key to a good TS algorithm is usually quite problem-specific policies and memory structures, there is no existing general TS tool available. Therefore more details of the TS approach used in this paper are discussed than the GA approach. The results obtained with these two methods are compared. The results illustrate that both methods are powerful tools for solving these kinds of problems. However TS is more efficient than GA in computation. The materials in this paper have been published in 19.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it