MétaCan
Menu
Back to cohort
Record W2019045459 · doi:10.1109/tcst.2012.2209178

Component-Based Method for the Modeling and Control of Modular Production Systems

2012· article· en· W2019045459 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 Control Systems Technology · 2012
Typearticle
Languageen
FieldComputer Science
TopicPetri Nets in System Modeling
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsComponent (thermodynamics)Modular designComponent-based software engineeringComputer scienceSystems engineeringSoftware developmentSoftware engineeringSoftware systemHierarchical control systemControl systemSoftwareControl (management)EngineeringProgramming languageArtificial intelligence

Abstract

fetched live from OpenAlex

Control software systems for manufacturing plants reveal common system architectural elements. This suggests taking advantage of a development paradigm based on reusable components. Creating and upgrading a repository of reusable components suitable for many systems that share common characteristics present a challenge for engineers, especially if composition mechanisms must be founded on control theories of discrete event systems and if formal synthesis tools must be integrated into well-accepted component-based software development processes. This paper explores a component model and a pragmatic method for the development of control software systems. The method, along with the underlying component model, fills the gap between a hierarchical control theory and component-based software engineering. A detailed case study developed around a Festo didactic learning modular production system demonstrates the relevance of the proposed approach to industrial application.

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)0.002
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: Methods · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.024
GPT teacher head0.264
Teacher spread0.240 · 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