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Record W2334235544 · doi:10.2514/6.2005-6959

Sky Spirit: Integration of UAV Design into an Aerospace Design Course

2005· article· en· W2334235544 on OpenAlexaff
Jeffrey Hammer, Demoz Gebre‐Egziabher, William L. Garrard, Scott E. Morgan

Bibliographic record

VenueInfotech@Aerospace · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced Aircraft Design and Technologies
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsCourse (navigation)AerospaceSkyComputer scienceSystems engineeringEngineeringAerospace engineeringAstronomyPhysics

Abstract

fetched live from OpenAlex

This paper discusses the efforts at the University of Minnesota, Twin Cities campus to integrate the design and operation of Uninhabited Aerial Vehicles (UAVs) into the undergraduate aerospace engineering curriculum. With the help of an industrial partner, a class project which involved developing an automatic pilot and guidance laws for a UAV was integrated into the senior design course. The UAV used was the Sky Spirit–a multipurpose aerial sensing platform being developed by the Lockheed-Martin Corporation. The end result has been a course which provides students the opportunity to exercise their engineering knowledge to the process of developing and testing an automatic pilot for a UAV. We discuss learning experiences that are general to all aspects of aerospace engineering and those elements unique to the design process of UAVs. Lessons learned and future plans for improving the curriculum are discussed.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.011
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.003

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 head0.026
GPT teacher head0.276
Teacher spread0.250 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations0
Published2005
Admission routes1
Has abstractyes

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