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Record W2572946383 · doi:10.1109/cgncc.2016.7828984

Wind estimation using the position information from a hovering quadrotor

2016· article· en· W2572946383 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsPropellerAerodynamicsControl theory (sociology)KinematicsPosition (finance)MATLABComputer scienceMotion (physics)Kinematics equationsFixed pointMarine engineeringEngineeringControl (management)Robot kinematicsMathematicsRobotMobile robotArtificial intelligenceAerospace engineeringPhysics

Abstract

fetched live from OpenAlex

In this paper, an approach to estimating the wind at a fixed place is proposed by using the observed position information of a hovering quadrotor. The aerodynamic characteristics of the propeller are firstly analyzed to establish the quadrotor model in a windy environment. Then, a PID method is adopted to maintain the stable hovering flight. Next, according to the hovering state equations, the wind estimating algorithm can be obtained by decomposing three-dimensional kinematics equations into two parts, one is the motion caused by wind, and another is the motion to keep itself hovering at a fixed point. Finally, the effectiveness of the proposed method is proved with the MATLAB/Simulink simulation.

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: none
Teacher disagreement score0.784
Threshold uncertainty score0.189

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 communication0.0000.001
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 head0.011
GPT teacher head0.212
Teacher spread0.201 · 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

Quick stats

Citations6
Published2016
Admission routes1
Has abstractyes

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