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Record W2972395987 · doi:10.1016/j.procs.2019.08.073

Simulating the charging of electric vehicles by laser

2019· article· en· W2972395987 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProcedia Computer Science · 2019
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsDalhousie University
Fundersnot available
KeywordsComputer scienceWireless power transferLaserMATLABWirelessPower (physics)Monochromatic colorPhotovoltaic systemAutomotive engineeringMaximum power transfer theoremElectrical engineeringCharging stationElectric vehicleTelecommunicationsPhysicsOpticsEngineering

Abstract

fetched live from OpenAlex

At present, wired and wireless charging methods for electric vehicles (EVs) have a number of drawbacks including short charging ranges and long charging times. Laser power transfer (LPT) is a wireless power transfer technique which can be used for UAV (unmanned aerial vehicle) and satellite charging. Recent developments in photovoltaic cells and laser technology allows the transfer power using light which might overcome many of the issues related with other charging methods. In this paper, we describe the design and implementation (in MATLAB) of a novel, high-power charging method using a laser (monochromatic light) to charge electric vehicles. The paper examines the overall efficiency of the LPT for a various input power-level. In addition, it also examines the safety and the possible charging infrastructure for LPT technology. Using this technique, we believe that an overall efficiency of 10-37% can be achieved using existing technology.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score0.305

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.188
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it