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Record W2009837231 · doi:10.1139/juvs-2013-0020

Implementing low cost two-person supervisory control for small unmanned aerial systems

2014· article· en· W2009837231 on OpenAlexvenueno aff
Brent Terwilliger, David Ison

Bibliographic record

VenueJournal of Unmanned Vehicle Systems · 2014
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsnot available
Fundersnot available
KeywordsAutopilotRedundancy (engineering)Systems engineeringControl (management)Computer scienceIdentification (biology)Proof of conceptControl systemTechnology readiness levelRisk analysis (engineering)EngineeringControl engineeringReliability engineering

Abstract

fetched live from OpenAlex

Commercially off-the-shelf remote control (RC) model aircraft have been used as a base platform for the development of small unmanned aerial systems (sUAS). Such designs have included use of first person view (FPV), inertial measurement units, and autopilot systems. Recommended guidelines established for operation of FPV recreational RC aircraft have applicability to operation of sUAS, when use of consistent components and platforms are considered. The purpose of this research was to examine existing literature, guidance, regulations, and other influencing factors to assess the necessity of redundancy management practices to identify recommended control stratagem, processes and procedures, operational criteria, and design of a proof of concept system to operate sUAS with optimal safety and operational benefits within recommended and legislated boundaries. Qualitative content analysis techniques were used to perform a literature review, while a survey of applicable technology (e.g., equipment, components, and software) was used in the development of a proof of concept system. The results were identification of a recommended supervisory control framework, a simulated supervisory control system, a physical proof of concept system, and a series of recommendations relating to considerations and potential follow-up research to better understand the limitations, constraints, and applicable benefits in the actual operation environment.

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 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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.301
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.053
GPT teacher head0.333
Teacher spread0.280 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations2
Published2014
Admission routes1
Has abstractyes

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