MétaCan
Menu
Back to cohort
Record W2791149106 · doi:10.22215/etd/2015-11115

Autonomous UAV Control for Low-Altitude Flight in an Urban Gust Environment

2015· dissertation· en· W2791149106 on OpenAlexaff
Syed Ali Raza

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsCarleton University
Fundersnot available
KeywordsAutopilotEngineeringPosition (finance)Computer scienceDroneSimulationControl engineeringAerospace engineeringMarine engineering

Abstract

fetched live from OpenAlex

With rapid advances in the unmanned aerial vehicle (UAV) field and their growing popularity in a wide range of civilian and commercial applications, UAV operation in urban areas is inevitable.For small-size UAVs conducting low-level flight in an urban landscape, wind disturbances pose a significant challenge.Ensuring safety while flying And then it was over.With congratulations from professors and colleagues, this doctoral research spanning over the past five years and eight months had successfully come to an end.Looking back there are many important individuals who played crucial roles in helping me reach the finish line.I will begin with a sincere thanks to Professor Joshua Marshall who most graciously recommended me to Professor Jason Etele.During my time working under the stellar supervision and mentorship of Professor Etele I have enjoyed a research environment with the freedom to explore and try new ideas.I would like to convey my utmost gratitude for your complete support and understanding throughout this endeavour.Your impressive ability to deduce control actions from flight test videos frame by frame taught me to pay attention to minute details in order to obtain meaningful results.Over many iterations you have helped me refine this thesis into its present state.A special thanks for bringing together our team of graduate students over memorable breakfast meetings, and feedback-rich progress report presentations, which helped me in improving my work over these years.Many thanks as well to Dr. Giovanni Fusina of DRDC for funding this research without which it would have been impossible for me to complete this work.I would like to acknowledge the MAE laboratory staff members, beginning with v Steve Truttmann for his friendship and overall helpfulness in conducting my laboratory teaching sessions.Stephan Biljan for helping in rotorcraft laboratory experiments and the 3D printing of replacement parts for TARA.I would also like to thank Alex Proctor and Kevin Sangster for their guidance in the machine shop

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.224
Teacher spread0.218 · 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 designSimulation or modeling
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

Citations19
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicAerospace and Aviation TechnologyFrench-language works237,207