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Record W2054589346 · doi:10.1115/imece2005-81619

Performance Evaluation of a Distributed Reconfigurable Controller Architecture for Robotic Applications

2005· article· en· W2054589346 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicReal-Time Systems Scheduling
Canadian institutionsNational Research Council CanadaUniversity of Saskatchewan
Fundersnot available
KeywordsReconfigurabilityEmbedded systemControl reconfigurationComputer scienceEthernetSynchronization (alternating current)Modular designModularity (biology)Controller (irrigation)Reference architectureComputer architectureSoftwareSoftware architectureDistributed computingComputer networkOperating system

Abstract

fetched live from OpenAlex

Recent research in controller architecture has had some focus on reconfigurability and associated concepts such as modularity and openness. These paradigms advocate non-proprietary components such as commercial off-the-shelves (COTS) with standard interconnection interfaces. The tradeoffs of such a controller architecture are performance challenges such as network-induced delays and synchronization problems, especially where non-real time entities such as Ethernet are involved. In our quest to address some of these challenges we have developed a modular control architecture for machine and robotic control as a test platform. The advantage of this architecture is cost-effectiveness and openness, achieved through the use of COTS components. Each machine axis is controlled by a real-time Java micro-controller and all the controllers communicate through a switched-Ethernet communication network. The architecture is designed to support reconfiguration of both hardware and software resources by the use of modularity and service-discovery protocols in the software and hardware design. Therefore devices such as axes and sensors may be reorganized, removed or added easily. Our research presents performance results and applications typical of industrial or real life for our control architecture. The performance criteria analyzed include network delays, synchronization resolutions and error analyses.

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 score0.898
Threshold uncertainty score0.307

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 head0.026
GPT teacher head0.275
Teacher spread0.248 · 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

Quick stats

Citations0
Published2005
Admission routes1
Has abstractyes

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