Hardware-in-the-Loop Emulation of Linear Induction Motor Drive for MagLev Application
Bibliographic record
Abstract
Linear induction machines are widely used in transportation systems due to their many advantages. Design and prototyping of electric machines are an expensive and time-consuming process; hardware-in-the-loop simulation provides an efficient alternative. In this paper, a field-programmable gate array-based real-time digital emulation of single-sided linear induction motor with the drive system is proposed. Implementation of the model is performed in both fixed-point using Xilinx system generator and floating-point number representations using a handwritten VHSIC Hardware Description Language code. Then, an evaluation in terms of real-time step-size and accuracy as well as hardware resource utilization is provided. The whole design was fully paralleled, which resulted in a considerable reduction of model execution time. The minimum time step of 2.3 and 0.8 μs was achieved for floating-point and fixed-point implementations, respectively. The results of the real-time simulation are verified by the experimental results as well as a 2-D finite-element simulation in JMAG software.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".