Optimal structure screening for large-scale multi-state series-parallel systems based on structure ordinal optimization
Why this work is in the frame
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Bibliographic record
Abstract
Multi-state series-parallel systems are widely-used for representing engineering systems. In real-life cases, engineers need to select an optimal system structure among many different multi-state series-parallel system structures. Screening of system structures is meaningful and critical. Moreover, to design a reliable structure, reliability evaluation is an indispensable part of the process. Due to the large number of available system structures, the computational burden can be huge when selecting the optimal one. Also, the number of components and possible states of each system can be enormous when the system scale is large, which causes significant complexity in exact reliability evaluation. To effectively select the optimal structure among numerous multi-state series-parallel systems under a reliability constraint, this article proposes an optimal structure screening method called the structure ordinal optimization. The proposed method combines the fuzzy universal generating function technique with an ordinal optimization algorithm. The fuzzy universal generating function technique is applied to reduce the computational time by approximately evaluating the reliability. Based on the approximate reliabilities, ordinal optimization helps to reduce the number of structure options and thus accelerate the screening process. Numerical examples show that the structure ordinal optimization method has advantages in computational efficiency with satisfactory accuracy.
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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 | 0.000 | 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 | 0.000 | 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