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Record W1194561882 · doi:10.4271/2015-01-2531

Model-based Method to Automate the Design of IMA Avionics System Based on Cosimulation

2015· article· en· W1194561882 on OpenAlexaff
Lin Bao, Guy Bois, Jean-François Boland, Julien Savard

Bibliographic record

VenueSAE International Journal of Aerospace · 2015
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsÉcole de Technologie SupérieurePolytechnique Montréal
Fundersnot available
KeywordsAvionicsComputer scienceSystems engineeringSoftware engineeringEngineeringEmbedded systemProgramming languageAerospace engineering

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">In the aerospace industry, as the modern avionics systems became more and more complex, the Integrated Modular Avionics (IMA) architecture has been proposed as a replacement of the federated architecture, in order to offer better solutions on SWaP constraints (Size, Weigh and Power). However, the development process of IMA avionics systems is much more difficult. This paper aims to propose to the aerospace industry a set of time-effective and cost-effective solutions for the integration and functional validation of IMA systems.</div><div class="htmlview paragraph">Based on MBE methodology, which is considered as an interesting solution for the IMA systems development [<span class="xref">8</span>], this paper proposes a design flow, that integrates three steps of refinement, for the configuration and the validation of IMA platforms. In the first step of the design flow, the modeling language AADL is used to describe the IMA architecture. The AADL modeling environment OCARINA, a code generator initially designed for the real-time operating system POK, has been modified to generate software integration code and system configuration files for the IMA simulator named SIMA. This solution is a cost effective alternative to expensive commercial development environments to validate ARINC653 software applications. In the second step of the design flow, a cosimulation platform composed of two simulators is proposed: Simulink for the simulation of peripherals and SIMA for the simulation of IMA modules. In the third step, the validated avionics applications and system configuration can be ported with minimum effort from the cosimulation environment to an implementation platform.</div><div class="htmlview paragraph">A case study, which consists in integrating several avionics applications to SIMA and then porting them to PikeOS development environment, was brought in the purpose of demonstrating the proposed design flows and co-simulation platform. The research work realized in this paper is a part of collaboration between industrials and academics through the CRIAQ AVIO509 project.</div></div>

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 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: Methods · Consensus signal: none
Teacher disagreement score0.954
Threshold uncertainty score0.392

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 head0.032
GPT teacher head0.297
Teacher spread0.265 · 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 teacher head, 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

Citations1
Published2015
Admission routes1
Has abstractyes

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