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Record W2115919109 · doi:10.1109/tsmcc.2007.900670

Toward Real-Time Distributed Intelligent Control: A Survey of Research Themes and Applications

2007· article· en· W2115919109 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 Systems Man and Cybernetics Part C (Applications and Reviews) · 2007
Typearticle
Languageen
FieldEngineering
TopicScheduling and Optimization Algorithms
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsReal-time Control SystemDistributed manufacturingControl (management)Computer scienceKey (lock)Scheduling (production processes)Intelligent controlSupply chainDistributed computingProcess managementSystems engineeringManufacturing engineeringEngineeringOperations managementComputer securityArtificial intelligenceBusiness

Abstract

fetched live from OpenAlex

Distributed intelligent control involves matching the control model more closely with the physical system. This is particularly relevant to industrial control systems that are required to control widely distributed devices in an environment that is prone to disruptions. Although considerable work has been conducted on the application of agent technology to the upper levels of manufacturing control (e.g., scheduling, planning, supply chain management, enterprise integration), the application of these techniques to the physical level of control where real-time constraints are prevalent is relatively new. This paper provides background on the work in this area, discusses key research themes in developing real-time distributed intelligent control systems for industrial applications, and concludes by sharing thoughts about the future prospects in this area.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.980
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.051
GPT teacher head0.308
Teacher spread0.257 · 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