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Record W2805149900 · doi:10.1109/plans.2018.8373465

An open source flight dynamics model and IMU signal simulator

2018· article· en· W2805149900 on OpenAlexaff
James G. McAnanama, Marsden Greg

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInertial Sensor and Navigation
Canadian institutionsWescam (Canada)
Fundersnot available
KeywordsComputer scienceSimulationSIGNAL (programming language)Flight simulatorOpen sourceInertial measurement unitSource modelAtmospheric modelDynamics (music)AcousticsArtificial intelligencePhysicsSoftwareMeteorology

Abstract

fetched live from OpenAlex

The offline development, validation and verification of the software for an airborne strapdown inertial navigation system (SINS) requires that the simulation of the inertial measurement unit (IMU) be based on representative flight dynamics data. This paper presents our efforts to combine an existing, popular and open-source flight dynamics model (FDM) with an IMU simulator we developed for the purpose of such work. We will show how it is possible to use the FDM and IMU simulator to develop, evaluate and improve SINS software. This development also allows for validation and verification of the end software product.

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)0.002
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: Methods · Consensus signal: Methods
Teacher disagreement score0.011
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.004

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 head0.009
GPT teacher head0.242
Teacher spread0.233 · 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
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

Citations7
Published2018
Admission routes1
Has abstractyes

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