Bibliographic record
Abstract
In order to solve the technical problem that the detection accuracy of electric vehicle BMS current sensor is not high enough to accurately measure large current and small current at the same time, a design scheme of a fluxgate current sensor with a single excitation core was provided. The topology design and working principle of the scheme were expounded, and the advanced nature of the scheme was proved. Compared with the current scheme, the H-bridge circuit was used to drive the excitation coil, which enabled the power supply of a single power supply and simplified the design of the power supply for the sensor. Moreover, an excitation detection circuit with double measurement resistors was constructed, which equivalently converted the excitation current ie flowing in the excitation coil into the difference between the two currents $\mathrm{i}_{\mathrm{e}1}$ and $\mathrm{i}_{\mathrm{e}2}$, and obtained the average value of the excitation current through the equivalent current difference $\mathrm{i}_{\mathrm{e}1}-\mathrm{i}_{\mathrm{e}2}$, thereby eliminating the zero drift of the current sensor, so that the small current identification and measurement accuracy of the current sensor can be improved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".