Design of Software for Calculating Tidal Currents in AT Power Supply Systems
Bibliographic record
Abstract
High-speed passenger railway, referred to as "high-speed rail", is a very important mode of transportation in today's society, which have attracted social attention and have been widely studied by domestic and foreign railway industry researchers. However, the transportation capacity and transportation conditions of high-speed railways are closely related to locomotive types, traction power supply systems, and traffic information control. Among them, the traction power supply system is an important source of its energy, which ensures the normal operation of the locomotive, so it has very important research significance. This article first introduces the AT power supply mode used in today's high-speed railways. Then the power flow calculation method and principle are derived as the design of the software below will pave the way. At the end, a complete power flow calculation method is summarized and implemented on the Visual Studio platform using C# language. A power flow calculation software for the AT power supply system was established, inputting parameters such as voltage and current, using Newton-Raphson method to iteratively calculate, and the final result can be directly displayed in the software, or exported and saved with Excel. And after the test, the software can run well, and the calculation speed is relatively fast, the result is normal, within the error range, and basically meets the expected requirements.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".