A weighted local steady‐state determination approach based on the globally optimal economic steady‐states
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Steady‐state incremental constraints of manipulated variables play a vital role in making given steady‐states satisfy physical limitations and avoiding drastic set‐point changes. Nevertheless, some research reveals that the steady‐state incremental constraints will make the given locally optimal economic steady‐states diverge from the globally optimal economic steady‐states. Therefore, a novel weighted local steady‐state determination approach based on the globally optimal economic steady‐states is presented in this paper. Firstly, the globally and locally optimal economic steady‐states are both evaluated through considering and not considering steady‐state incremental constraints. Then, the angle between them is evaluated and the closest local steady‐state from the globally optimal economic steady‐state is calculated. Subsequently, a new weighted local steady‐state is evaluated by combining the locally optimal economic steady‐state and the closest local steady‐state, in which the weighting coefficient is carefully tuned based on the above‐calculated angle. Finally, several simulations verify that the proposed method could effectively shorten the settling time of controlled systems and improve their economic performance.
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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