MétaCan
Menu
Back to cohort
Record W2024830930 · doi:10.1108/00022661011028074

Design and reliability prediction of a distributed landing gear control system

2010· article· en· W2024830930 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

VenueAircraft Engineering and Aerospace Technology · 2010
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsLanding gearReliability (semiconductor)Control (management)Control systemEngineeringReliability engineeringAirplaneComputer scienceAutomotive engineeringControl engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Purpose Proper function of landing gear plays a crucial role in the safe operation of an airplane. Traditional landing gear control system utilizes centralized control technology. The relatively heavy wire harness and low reliability accompanied with this technology make it logical to transfer from traditional control to real‐time distributed control. This paper aims to look into a new landing gear control system based on time‐triggered architecture (TTA). Design/methodology/approach In this paper, a new landing gear control system based on TTA is proposed. The reliability of the proposed system is investigated using a combination of Markov analysis and MIL‐HDBK‐217 methods. Findings The results show that by integration of TTP/C and TTP/A technologies, the advantages of both are achieved. A very high level of reliability is obtained. This increases the confidence when adopting distributed landing gear control technology. Originality/value The paper presents a new landing gear control system based on TTA, the reliability of which is very high.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score0.601

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 head0.002
GPT teacher head0.153
Teacher spread0.151 · 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