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Record W1559610976 · doi:10.1109/mcs.2004.1337866

A force-feedback joystick for control and robotics education - affordable software for educational institutions

2004· article· en· W1559610976 on OpenAlexaff
Peter Osborne, Roy D. McLellan, N. McEvoy, Keyvan Hashtrudi-Zaad

Bibliographic record

VenueIEEE Control Systems · 2004
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsQueen's University
Fundersnot available
KeywordsJoystickSoftwareMATLABControl (management)Computer scienceRoboticsVirtual realitySimulationHuman–computer interactionControl engineeringSoftware engineeringArtificial intelligenceEngineeringRobotOperating system

Abstract

fetched live from OpenAlex

This article describes a project that converts low-cost commercially available force feedback joysticks into flexible experimental facilities for control systems education and virtual reality simulations without hardware modifications. The software allows educational institutions to offer affordable hands-on laboratories, featuring a traditional or discovery-learning approach. With intuitive software running in the MATLAB environment, it is now possible to introduce large undergraduate classes to control engineering.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.004

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 head0.011
GPT teacher head0.244
Teacher spread0.233 · 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 designNot applicable
Domainnot available
GenreSoftware

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

Citations3
Published2004
Admission routes1
Has abstractyes

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