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Record W2125275327 · doi:10.1109/dis.2006.4

A New Evaluation Method of Communication for Distributed Control

2006· article· en· W2125275327 on OpenAlexaff
Jason J. Scarlett, Robert W. Brennan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicReal-Time Systems Scheduling
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer scienceEvent (particle physics)Communications protocolUnified Modeling LanguageCAN busDistributed computingProtocol (science)Telecommunications networkMeasure (data warehouse)Control (management)Controller (irrigation)Communications systemEmbedded systemReal-time computingComputer networkSoftwareOperating systemDatabase

Abstract

fetched live from OpenAlex

Distributed systems are relying more heavily on event-triggered system architectures such as UML and IEC 61499. Existing communication protocols can support the high-level communication within these systems, but there is contention as to which low-level protocol to use, or if any exist that meet the requirements of being event-triggered and hard realtime. This paper presents a new way to measure communication performance. The goal of the new measurement method is to stress the necessity that a system be both efficient and fair. This is illustrated by comparing three communication strategies; controller area network (CAN), time-triggered CAN (TTCAN), and alternating priority CAN. The first two represent the extremes between event-triggered and time-triggered communication strategies. The third is introduced to illustrate the benefits of the new measurement technique

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)0.005
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0030.006
Open science0.0020.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.326
Teacher spread0.300 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations5
Published2006
Admission routes1
Has abstractyes

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