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Record W2558693562 · doi:10.1109/lra.2016.2633623

Design of a Passive Vertical Takeoff and Landing Aquatic UAV

2016· article· en· W2558693562 on OpenAlexaffabout
Richard-Alexandre Peloquin, Dominik Thibault, Alexis Lussier Desbiens

Bibliographic record

VenueIEEE Robotics and Automation Letters · 2016
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsTakeoffTakeoff and landingWingMarine engineeringAerospace engineeringAeronauticsFlight control surfacesSimulationEngineeringEnvironmental scienceComputer scienceAutomotive engineeringAerodynamics

Abstract

fetched live from OpenAlex

With the goal of extending unmanned aerial vehicles mission duration, a solar recharge strategy is envisioned with lakes as preferred charging and standby areas. The Sherbrooke University Water-Air VEhicle (SUWAVE) concept developed is able to takeoff and land vertically on water. The physical prototype consists of a wing coupled to a rotating center body that minimizes the added components with a passive takeoff maneuver. A dynamic model of takeoff, validated with experimental results, serves as a design tool. The landing is executed by diving, without requiring complex control or wing folding. Structural integrity of the wing is confirmed by investigating the accelerations at impact. A predictive model is developed for various impact velocities. The final prototype has executed multiple repeatable takeoffs and has succeeded in completing full operation cycles of flying, diving, floating, and taking off.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.019
GPT teacher head0.208
Teacher spread0.188 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations50
Published2016
Admission routes2
Has abstractyes

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