MétaCan
Menu
Back to cohort
Record W2249258697 · doi:10.1109/smc.2015.77

Modelling a Quadrotor Vehicle Using a Modular Deep Recurrent Neural Network

2015· article· en· W2249258697 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
FieldPhysics and Astronomy
TopicModel Reduction and Neural Networks
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMIMOModular designCurse of dimensionalityArtificial neural networkComputer scienceControl theory (sociology)Noise (video)Rotor (electric)Deep learningControl engineeringInput/outputRecurrent neural networkArtificial intelligenceEngineeringTelecommunicationsChannel (broadcasting)

Abstract

fetched live from OpenAlex

In this paper, the Modular Deep Recurrent Neural Network (MODERNN) framework is studied for learning a Multi-Input-Multi-Output (MIMO) model of a quad rotor. Comparing a Single-Input-Single-Output (SISO) system, a MIMO system is much harder to model because of the intercoupling of the system variables as well as the multi-dimensionality of the input and output spaces. In this paper it is shown that the MODERNN framework is capable of modelling complex MIMO dynamical mappings, such as a simulated MIMO model (4-by-4) of a quad rotor vehicle in the presence of noise and ground effect.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.544

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 head0.077
GPT teacher head0.282
Teacher spread0.205 · 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

Citations19
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicModel Reduction and Neural NetworksFrench-language works237,207