Non-linear programming for filter management in a fluid power system with uncertainty
Bibliographic record
Abstract
In this study, non-linear programming for filter management in a fluid power system is developed for the assessment of filter allocation and replacement strategies in a fluid power system (FPS). The developed model can effectively tackle uncertainties existing as interval values and fuzzy sets, through introducing independent control variables and L- R fuzzy numbers into the interval non-linear programming (INP) framework. Moreover, in the modelling formulation, the service life of the pump and the cost of filter maintenance are included in the objective function to reflect the effect of strategies. The developed method is applied to the filter management of an FPS with a bypass filtration system. The results indicate that reasonable solutions have been generated. Not only can they help identify optimal filter allocation and replacement strategies to control the contamination of FPSs, but also they can provide decision makers more information regarding trade-offs among system cost, certainty and safety in comparison with the INP 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 | 0.000 | 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 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".