An effective identification of the induction machine parameters using a classic genetic algorithm, NSGA II and θ-NSGA III
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
To remain competitive, the manufacturing industry is using computer processing power to innovate, develop and optimize new cost-efficient production strategies. This is the reason why optimization of automation systems is deployed to improve productivity, quality and robustness of the production. The different existing goals of optimization as the control machine, management of the power consumption, design of electrical installation and prediction of motor faults lead to the necessity of estimating the induction machine parameters (the stator and rotor resistances, the stator and rotor inductances and the magnetizing inductance). To these ends, researchers and companies are investigating efficient methods to identify these parameters. In this paper, we propose an effective method for the induction machine parameters identification based on the new θ-NSGA III genetic algorithm. A comparison between a classic single objective genetic algorithm (GA) and two well-known multi-objectives GAs (NSGA II and θ-NSGA III) is performed. Our results show that the multi-objective GA θ-NSGA III provides a better estimation of parameters than the classic single objective GA and the multi-objective GA NSGA II.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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