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Record W2783680482 · doi:10.1051/epjconf/201817003004

Radiation-Tolerance Assessment of a Redundant Wireless Device

2018· article· en· W2783680482 on OpenAlexafffund
Qiang Huang, Jin Jiang

Bibliographic record

VenueEPJ Web of Conferences · 2018
Typearticle
Languageen
FieldEngineering
TopicRadiation Effects in Electronics
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaUniversity Network of Excellence in Nuclear Engineering
KeywordsReliability engineeringWirelessRedundancy (engineering)Reliability (semiconductor)Nuclear power plantRadiationComputer scienceElectronicsNuclear powerEngineeringPower (physics)Electrical engineeringTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

This paper presents a method to evaluate radiation-tolerance without physical tests for a commercial off-the-shelf (COTS)-based monitoring device for high level radiation fields, such as those found in post-accident conditions in a nuclear power plant (NPP). This paper specifically describes the analysis of radiation environment in a severe accident, radiation damages in electronics, and the redundant solution used to prolong the life of the system, as well as the evaluation method for radiation protection and the analysis method of system reliability. As a case study, a wireless monitoring device with redundant and diversified channels is evaluated by using the developed method. The study results and system assessment data show that, under the given radiation condition, performance of the redundant device is more reliable and more robust than those non-redundant devices. The developed redundant wireless monitoring device is therefore able to apply in those conditions (up to 10 M Rad (Si)) during a severe accident in a NPP.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.701
Threshold uncertainty score0.408

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 head0.010
GPT teacher head0.270
Teacher spread0.261 · 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

Citations1
Published2018
Admission routes2
Has abstractyes

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