Integrated Modular Avionics - Past, present, and future
Bibliographic record
Abstract
IMA (Integrated Modular Avionics) approaches have been around for 30 years but vary widely in implementation and the extent of both hardware and software levels of unification. The IMA concept, which replaces numerous separate processors and line replaceable units (LRUs) with fewer, more centralized processing units, has led to significant weight reduction and maintenance savings in both military and commercial airborne platforms. The IMA concept for this definition originated in the United States with the F-22 Joint Integrated Avionics Working Group (JIAWG) 30 years ago and then migrated to business jets and commercial transports in the late 1990s. In the last 10 years, the mainstream IMA definition has incorporated time and space partitioned software environments based on the ARINC 653 standard. During this period, software complexity has exploded. The ARINC 653 extended IMA definition has enabled the development of common software infrastructure to enhance complex systems management and enable greater software reuse. In open literature, civil aviation has cornerstone IMA extended examples in the Airbus 380 and Boeing 787 Dream-liner platforms. This article provides a summary of IMA history, presents these new IMA challenges going forward, and summarizes research focus exploring advanced IMA solutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".