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Record W1985101388 · doi:10.1243/0954405001518026

Intelligent control for holonic manufacturing systems

2000· article· en· W1985101388 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

VenueProceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture · 2000
Typearticle
Languageen
FieldEngineering
TopicFlexible and Reconfigurable Manufacturing Systems
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsReconfigurabilityControl reconfigurationAdaptabilityComputer scienceControl systemEmbedded systemDistributed control systemControl (management)Industrial control systemArchitectureBlock (permutation group theory)Distributed computingEngineeringOperating systemArtificial intelligence

Abstract

fetched live from OpenAlex

To enable holonic manufacturing systems (HMS) to have their desired characteristics of adaptability and reconfigurability, their control systems need to be dynamically reconfigurable. An holonic control system implementing metamorphic mechanisms which facilitate such reconfiguration has therefore been developed conceptually and is presented in this paper. An integrated and generic event-driven control architecture is described for the various functional levels of this distributed holonic control system. The architecture utilizes the emerging IEC 1499 function block standard to specify the requisite behaviour of the distributed-control software components. The core low-level control mechanisms have been implemented within an agent-based distributed real-time operating system and a prototype holonic control system using the new architecture has been developed and tested.

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 categoriesMeta-epidemiology (narrow)
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 score0.905
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.191
Teacher spread0.183 · 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