Bibliographic record
Abstract
Advanced transportation technologies, such as hybrid-electric vehicles, battery-electric vehicles, and fuel cell vehicles, rely on high-power DC/DC converters to interconnect and manage their power systems. Boost converters are especially useful in stepping-up the voltage from a battery or fuel cell to match the electric motor voltage range. However, conventional high-power boost converters are very large and heavy, partly due to the large inductors used in the design. These heavy components negatively affect the fuel economy of the vehicles, add cost to the vehicle, and may add difficulty for packaging. The solution is to implement a method which reduces the switching losses in the converter so that the switching frequency can be increased and hence the size of the inductors, and overall converter, can be reduced. This paper proposes a new lossless snubber circuit that greatly reduces the switching losses of the IGBT in the converter. The circuit uses no additional magnetic components, has a simple control strategy, is relatively low-cost, and is shown to provide a boost in efficiency even while greatly reducing the size of the converter. Thermal considerations are taken into account to provide practical simulation results
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 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 | 0.000 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".