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Record W2329678519 · doi:10.7227/ijmee.40.4.4

Linear-Graph Modeling Paradigms for Mechatronic Systems

2012· article· en· W2329678519 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Mechanical Engineering Education · 2012
Typearticle
Languageen
FieldComputer Science
TopicModeling and Simulation Systems
Canadian institutionsUniversity of British Columbia
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of Canada
KeywordsMechatronicsComputer scienceControl engineeringGraphDomain (mathematical analysis)Bond graphSystems modelingGraph theorySystem dynamicsTheoretical computer scienceModeling and simulationSystems engineeringArtificial intelligenceEngineeringSoftware engineeringSimulationMathematics

Abstract

fetched live from OpenAlex

Concepts of mechatronics are applicable to complex and multi-domain dynamic systems. Proper modeling of such systems can benefit analysis, simulation, design and control of these systems. The linear-graph (LG) approach is particularly suitable in mechatronic modeling because it presents a unified way to represent different domains and also provides a direct correspondence to the topology of the physical components of a system. This paper presents some useful concepts of graph trees and generalized equivalent circuits as applied to LGs, which can facilitate modeling of mechatronic systems. The significance and application of the presented paradigms of modeling are indicated using examples. These paradigms have been integrated into both an undergraduate course and a postgraduate course on the modeling of dynamic systems. Students have appreciated this integrated and systematic approach to modeling of multi-domain (mechatronic) systems.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.920
Threshold uncertainty score0.389

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 communication0.0000.001
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 head0.029
GPT teacher head0.296
Teacher spread0.267 · 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