A New Optimization Design Methodology Of Locomotive Transformers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The present study develops a new genetic algorithm, Gene Controlling Genetic Algorithm (GCGA) for optimization design. In performing engineering design optimization, this genetic algorithm intends to maximize the utilization of the existing information such as practical design knowledge, existing relationships of design parameters and engineering experience. With GCGA, design optimization process becomes much efficient. Application of GCGA in optimizing an electric locomotive transformer design is provided in this study. Detailed procedures of optimization with GCGA are presented. In comparing with the traditional genetic algorithm such as EPGA, as indicated in the study, GCGA provides a global convergent solution with higher stability and faster speed. It is demonstrated that GCGA is an algorithm specifically suitable for optimizing complex designs in engineering practice.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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