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Record W2131053966 · doi:10.1109/robot.1994.351128

A hybrid supervisory control system for flexible manufacturing workcells

2002· article· en· W2131053966 on OpenAlexaff
R.A. Williams, B. Benhabib, K.C. Smith

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicPetri Nets in System Modeling
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSupervisory controlNondeterministic algorithmWorkcellController (irrigation)Supervisory control theoryControl engineeringControl (management)Control theory (sociology)Control systemComputer scienceEngineeringRobotArtificial intelligenceTheoretical computer science

Abstract

fetched live from OpenAlex

Because of their nondeterministic nature of behaviour, the supervisory control of manufacturing systems must be carried out in closed loop. This trait greatly increases the size and complexity of the discrete-event system (DES)-based supervisory-controllers of manufacturing systems. In an attempt to cope with this complexity, a hybrid supervisory controller (HSC) is proposed. It splits operations between its three elements: (i) a DES supervisory controller, which contains the nominal control strategy, (ii) a diagnostic system, which monitors the workcell, and (iii) an alternate strategy driver (ASD), that asserts control by revising the nominal strategy of the DES supervisory controller, whenever events diverge from the states of the DES supervisory controller. This modified approach to DES control provides a more efficient controller than could reasonably be attained using solely a DES supervisory controller.>

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.048
GPT teacher head0.225
Teacher spread0.177 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations12
Published2002
Admission routes1
Has abstractyes

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