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Record W2167818703 · doi:10.1109/9.964697

Stochastic stability analysis of fault-tolerant control systems in the presence of noise

2001· article· en· W2167818703 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

VenueIEEE Transactions on Automatic Control · 2001
Typearticle
Languageen
FieldEngineering
TopicFault Detection and Control Systems
Canadian institutionsWestern University
Fundersnot available
KeywordsExponential stabilityControl theory (sociology)Markov processNoise (video)MathematicsLyapunov functionStability (learning theory)Fault detection and isolationStochastic processExponential functionApplied mathematicsComputer scienceStatisticsControl (management)Mathematical analysisNonlinear system

Abstract

fetched live from OpenAlex

The stochastic stability of fault tolerant control systems (FTCSs) in the presence of noise are,analyzed using the Lyapunov function approach. In particular, the FTCS with a Markovian fault detection and isolation process having transition probabilities conditioned on the state of another Markovian process representing component failures, is considered. The almost sure asymptotic stability in probability and the exponential stability in the mean square are considered. Specifically, a testable necessary and sufficient condition for the exponential stability in the mean square is derived. A numerical example is presented to illustrate the theoretical analysis.

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: none
Teacher disagreement score0.626
Threshold uncertainty score0.691

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.225
Teacher spread0.214 · 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