Bibliographic record
Abstract
In order to minimize the greenhouse effect due to the emission of CO2, automobile manufactures have been developing battery-powered plug-in automobiles with re-chargeable Lithium Polymer batteries. However, these pure electric vehicles (EVs) are not welcomed in the market because the Lithium batteries are heavy and still expensive with limited rechargeable cycles. Furthermore, charging time and relatively short driving range obstruct the commercialization of EVs. To solve the problems, KAIST proposed four generations of On-Line Electric Vehicles (OLEVs), a sort of roadway wireless powered EVs. This paper summarizes the progress of the OLEV developments. Four generations of OLEV were demonstrated for different underground electric power rails and pick-ups. The air-gap of the 1st generation OLEV car is 1 cm and the input to output power efficiency is 80 % with 3 kW output power. The ultra slim U-type mono rail applied to the 2nd generation OLEV bus gives 52 kW output power with 72 % efficiency at 17 cm air-gap. The ultra slim W-type dual rail applied to the 3rd generation OLEV sports utility vehicle delivers 15 kW/pick-up power with 71 % efficiency at 17 cm air- gap, which has recently been improved to 83 % at 20 cm air-gap for an OLEV bus, and 74 % efficiency at 12 cm air-gap for an OLEV train, respectively. The 4th generation of OLEV bus is under development, which has very narrow I-type underground power rails and compact high power pick-ups with drastically reduced electro-magnetic field (EMF), lower construction cost, small installation time, and enhanced lateral displacement. Throughout the development of OLEV, it has shown that low cost EV without high reliance on batteries with sufficiently high power capacity, efficiency, and air-gap is possible, which were the major obstacles of the roadway powered EV for practical applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.015 |
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".