9 Parameters estimation of an extended induction machine model using genetic algorithms
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
Industries are innovating, developing and optimizing production line to improve productivity, quality and robustness of the production in order to be competitive. The different existing goals of optimization, such as the computation of closed-loop drive-fed motors, the reduction of energy consumption or the detection of motor faults, lead to the necessity to identify the induction machine parameters (resistance, inductances, ...). To these ends, researchers and companies are investigating efficient methods to identify these parameters. In this paper, we propose for the first time an effective identification of 9 parameters of the extended induction machine model based on the θ-NSGA III. In addition, a comparison between a classic genetic algorithm, the well-known NSGA II and the θ-NSGA III is performed. Results show that the θ-NSGA III provides a better estimation of parameters than the two other genetic algorithms.
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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