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Record W4408773952 · doi:10.48175/ijarsct-24410

Foundation Fieldbus with Control-In-Field and Control-In-Controller-An Analysis

2025· article· en· W4408773952 on OpenAlexaff
Stéfane Nascimento d, Chaitanya J. Talati, M. Rajendra Prasad, Prof. Chirag S. Dalal

Bibliographic record

VenueInternational Journal of Advanced Research in Science Communication and Technology · 2025
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsCarbon Engineering (Canada)
Fundersnot available
KeywordsFOUNDATION fieldbusFoundation (evidence)Foundation Fieldbus H1FieldbusControl (management)Field (mathematics)Controller (irrigation)EngineeringControl engineeringComputer scienceControl systemPolitical scienceElectrical engineeringArtificial intelligenceMathematicsLaw

Abstract

fetched live from OpenAlex

Foundation Fieldbus (FF) is an advanced digital communication protocol widely used in industrial automation, particularly in process control industries.[1] It enables enhanced communication between field devices and control systems, offering improved efficiency, reliability, and scalability. One of the key advantages of Foundation Fieldbus is its ability to support distributed control architectures, allowing control strategies to be implemented either at the field level (Control In Field, CIF) or at the central control system (Control In Controller, CIC).Control In Field (CIF) leverages smart field devices with built-in control capabilities, allowing control loops to be executed locally without relying on a centralized controller.[2] This reduces communication latency, minimizes bandwidth usage, and enhances system resilience against network failures.[3] CIF enhances plant availability, as the failure of a central controller does not disrupt local control operations. On the other hand, Control In Controller (CIC) involves executing control logic at the central Distributed Control System (DCS) or Programmable Logic Controller (PLC). This traditional approach simplifies system management, enables easier modifications to control strategies, and allows centralized monitoring and diagnostics.[4]The choice between CIF and CIC significantly impacts system performance, reliability, and maintenance complexity. This study provides an in-depth evaluation of CIF vs. CIC by analyzing their impact on system performance, fault tolerance, response time, and overall operational efficiency

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 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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.002
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 head0.013
GPT teacher head0.368
Teacher spread0.355 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes1
Has abstractyes

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