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Record W2960438136 · doi:10.1109/meco.2019.8760287

On the Reliability and Flexibility of FPGAs for Fault Tolerance in Sectored Networked Control Systems

2019· article· en· W2960438136 on OpenAlex
Rana H. ElMaraashly, Gehad I. Alkady, Ramèz M. Daoud, Hassan H. Halawa, Hassanein H. Amer, Ihab Adly, Tarek K. Refaat

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
FieldEngineering
TopicRadiation Effects in Electronics
Canadian institutionsCisco Systems (Canada)
Fundersnot available
KeywordsFault toleranceRedundancy (engineering)Modular designReliability (semiconductor)Reliability engineeringField-programmable gate arrayEmbedded systemFlexibility (engineering)Computer scienceControl systemActuatorReliability block diagramTriple modular redundancyEngineeringFault tree analysisPower (physics)

Abstract

fetched live from OpenAlex

As Networked Control Systems grow more complex in industrial applications, moving network modules and cabling decrease overall system reliability. This paper presents an FPGA-based fault tolerance technique to reduce cabling and increase the overall reliability of such a system. A sectored sensor-to-actuator networked control system architecture with sensor-level sift-out modular redundancy is modeled and analyzed. The fault models considered in this study are Single Event Upsets and hard failures. A reliability analysis is then conducted to evaluate the reliability of each block in the system and study the overall system reliability. A generic reliability analysis is presented to investigate the flexibility of the fault tolerance technique and a case study demonstrates the reliability improvements over a system that does not utilize FPGAs and cable reduction.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Simulation or modelinglow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Simulation or modelinghigh
models agreeAgreement compares identical category sets and study designs across arms.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.239

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 head0.005
GPT teacher head0.208
Teacher spread0.203 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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