Modelling and Simulation of Electrical Machines, Converters and Power Systems
Bibliographic record
Abstract
The ninth International Conference organized by the Technical Committee n 1 (TC1) of IMACS, ELECTRIMACS took place at Quebec City, Canada, from June 9th to 11th 2008. The goal of this conference was to provide scientific and professional interaction for the advancement of the modeling and simulation in electrical power engineering. Approximately, 130 technical papers were presented in the areas of computer-aided design and optimization, control, modeling, simulation and monitoring of power systems, static power converters, electrical machines, electromechanical systems and drives. The IMACS TC1 members wish to express their deepest thanks to Prof. Hoang Le-Huy, Conference Chair, and Prof. Philippe Viarouge, Technical Program Chair, for their relentless efforts resulting in a successful event which took place in the beautiful city of Quebec. This special issue of Mathematics and Computers in Simulation proposes 23 papers that were originally presented at the ELECTRIMACS 2008 conference. During the conference, the authors were invented to send a revised version of their paper. The final selection was made after submitting the revised papers to a thorough peer-review process. The Guest Editors express their deepest thanks to all the reviewers and to all the authors, successful or not, who submitted papers and contributed to this Special Issue. Next ELECTRIMACS Conference, will be held in Cergy-Pontoise, France in June 2011.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".