Engine speed reduction for hydraulic machinery using predictive algorithms
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents an analysis of the potential for engine speed reduction in hydraulic equipment, taking into account not only the minimum engine speed required to meet the current flow demand, but also the minimum speed capable of accelerating the engine to meet increased flow demand in the near future. This is a predictive task, as it requires an estimate of the operator's intention to increase flow demand. We present an analysis of the potential for engine speed reduction using a work cycle from a 40 ton excavator loading a truck, which results in a potential 33% reduction in the mean engine speed with no reduction in useful work rate. We also present two new engine speed control algorithms to perform this predictive task. These controllers are easy to tune and require only a small amount of information about the plant and work cycle. A simulation study is performed that demonstrates the controller's performance and studies the effect of tuning parameters.
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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