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Record W2379476340

Research of Virtual Instrument System for Large-scale Aerotransport Integrated Training Simulator

2007· article· en· W2379476340 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

VenueJisuanji fangzhen · 2007
Typearticle
Languageen
FieldEngineering
TopicSimulation and Modeling Applications
Canadian institutionsL'Alliance Boviteq
Fundersnot available
KeywordsComputer architecture simulatorSimulationProcess (computing)Virtual instrumentComputer scienceInstrument DriverVirtual realitySoftwareScale (ratio)Human–computer interactionOperating system
DOInot available

Abstract

fetched live from OpenAlex

Along with the rapid development of computer technology,virtual simulators are used widely for military training.At present,the instrument system of simulator is mostly real instrument system that has some disadvantages of complex driving and high price.According to the development of large-scale aerotransport integrated training simulator in which the virtual instrument system is used,the simulator architecture,in which the real instrument is replaced by virtual instrument is introduced in the paper.The development process of virtual instrument by means of GL Studio3.0 software,mathematical models and man-machine interactive methods are presented.Displaying methods of needle,digital wheel and nonlinear graduation are introduced through the example of developing some classic instruments such as altitude indicator,horizon sensor etc,and some corresponding C++ codes are given.A new method is provided to implement the instrument simulation of training simulator.

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)0.001
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 score0.374
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.089
GPT teacher head0.355
Teacher spread0.266 · 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