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Record W2115467047 · doi:10.1109/icsmc.2009.5346576

An autonomous agent-based framework for self-healing power grid

2009· article· en· W2115467047 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware System Performance and Reliability
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsInterdependenceComputer scienceGridCascading failureSelf-healingDistributed computingElectric power systemSmart gridService (business)Asset (computer security)Multi-agent systemComputer securityRisk analysis (engineering)Reliability engineeringPower (physics)EngineeringArtificial intelligenceBusiness

Abstract

fetched live from OpenAlex

Reliable, secure and robust power grid network is a necessity for crucial financial, industrial and business networks. Since national electrical grid, telecommunication, information networks and transportation networks are interdependent critical infrastructures, having an agent-based self-healing framework to reduce cascading failures through the networks and finding reasonable solution for potential faults - would be an essential asset. In response to this need we propose a self-healing framework that employs advanced failure diagnosis techniques along with autonomous Web services to provide temporary recovery solutions. Furthermore, it provides a cognitive planning cycle to find ultimate corrective solutions as well as evaluation service to verify the effectiveness and performance of the final solution.

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: Methods · Consensus signal: none
Teacher disagreement score0.848
Threshold uncertainty score0.441

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 science0.0010.000
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 head0.012
GPT teacher head0.281
Teacher spread0.269 · 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