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Record W3192330224

Frequency domain system identification of fixed-wing unmanned aerial vehicles

2014· dissertation· en· W3192330224 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMspace (University of Manitoba) · 2014
Typedissertation
Languageen
FieldEngineering
TopicControl Systems and Identification
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsFixed wingIdentification (biology)WingAeronauticsAerospace engineeringMarine engineeringComputer scienceGeographyEngineeringBiologyEcology
DOInot available

Abstract

fetched live from OpenAlex

The goal of this thesis is to identify airplanes’ reduced order transfer functions, and aerodynamic derivatives in the longitudinal channel. The outcome of the research will benefit aircraft systems’ controller design, modeling and simulation. To identify the system transfer functions and aerodynamic derivatives, direct and indirect frequency domain identification methods are applied. For the direct method, the Equation Error (EE) method is adopted to process the Cropcam’s input-output data pairs and identify the aerodynamic derivatives from the flight data directly. The indirect approach is called the Transfer Function (TF) method. The derivatives identified by the EE method and transfer function method are compared with the ones computed from a Vortex Lattice based program called AVL. The identification results are further verified by comparing computer simulation outputs with flight test responses.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.937
Threshold uncertainty score1.000

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 head0.006
GPT teacher head0.172
Teacher spread0.166 · 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