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Record W2325216839 · doi:10.1016/j.promfg.2015.07.146

Beyond Line of Sight Control of Small Unmanned Aerial Vehicles Using a Synthetic Environment to Augment First Person Video

2015· article· en· W2325216839 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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueProcedia Manufacturing · 2015
Typearticle
Languageen
FieldEngineering
TopicRobotics and Sensor-Based Localization
Canadian institutionsMemorial University of Newfoundland
FundersNational Research Council CanadaNational Science Council
KeywordsAugmentSightLine-of-sightLine (geometry)Computer graphics (images)Artificial intelligenceComputer scienceComputer visionEngineeringAerospace engineeringPhysics

Abstract

fetched live from OpenAlex

This paper is a summary of efforts to develop alternative methods to control small Unmanned Aerial Vehicles (UAV) 1 in manual mode while at Beyond Line of Sight (BLOS) range. While it is true that the majority of the UAV airborne activities will be in autonomous mode (i.e. using an autopilot) this may not always be the case, especially if there is a failure of the autopilot or need for an emergency manual override maneuver. This requirement for an emergency manual back-up mode during all flight stages remains in proposed UAV regulations being defined in the U.S., Canada and Europe. This paper proposes a possible manual pilot console using a combination of an extended-range First Person View (FPV) video augmented by a synthetic simulation environment. Practical field testing of the various elements which make up this system is presented.

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 score0.567
Threshold uncertainty score0.737

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.023
GPT teacher head0.196
Teacher spread0.173 · 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