Machine Learning Building Blocks for Real-Time Emulation of Advanced Transport Power Systems
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 revolution of artificial intelligence (AI) is transforming major industries worldwide. With accurate inferencing, AI has caught the attention of many engineers and scientists. Promisingly, hardware-in-the-loop (HIL) emulation can adopt this type of modeling method as one of the alternatives after comprehensive investigation. This paper proposes an approach for emulating power electronic motor drive transients for advanced transportation applications (ATAs) using machine learning building blocks (MLBBs) without any traditional circuit-oriented transient solver. The more electric aircraft (MEA) power system is chosen as a case study to validate the real-time emulation performance of MLBBs. Inside MLBBs, neural networks (NNs) have been applied to build component-level, device-level, and system-level models for various equipment. These models are well trained in a cluster and transplanted into the field-programmable gate array (FPGA) based hardware platform. Finally, MLBB emulation results are compared with PSCAD/EMTDC for system-level and SaberRD for device-level, which showed high consistency for model accuracy and high speed-up for hardware execution.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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