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Record W2094069839 · doi:10.1109/tsmcc.2011.2174982

Application-Oriented Intelligent Middleware for Distributed Sensing and Control

2012· article· en· W2094069839 on OpenAlex
Ningxu Cai, Mohammad Gholami, Litao Yang, Robert W. Brennan

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 Systems Man and Cybernetics Part C (Applications and Reviews) · 2012
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMiddleware (distributed applications)Computer scienceAutomationWireless sensor networkSoftware deploymentDistributed computingEmbedded systemFactory (object-oriented programming)Industrial control systemTask (project management)Control (management)Computer networkSoftware engineeringSystems engineeringEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, wireless sensor networks are proposed as a distributed sensing and control (DSC) approach for productivity and safety improvement of harsh and dynamic industrial systems, such as factory automation, oil and gas industries, and wind farms. The proposed approach focuses on DSC middleware, which considers both application requirements and network resource constraints. By embedding complex application knowledge at different levels and configuring network topology in real time, the DSC system can accomplish effective task assignment, optimal network deployment, and device-level intelligence. IEC 61499 function blocks and intelligent agents are employed as modeling tools for the middleware implementation.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.778

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.018
GPT teacher head0.245
Teacher spread0.227 · 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